Tree

BaseDecisionTree

Module Sklearn.​Tree.​BaseDecisionTree wraps Python class sklearn.tree.BaseDecisionTree.

type t

apply

method apply

val apply :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the index of the leaf that each sample is predicted as.

.. versionadded:: 0.17

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

cost_complexity_pruning_path

method cost_complexity_pruning_path

val cost_complexity_pruning_path :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Compute the pruning path during Minimal Cost-Complexity Pruning.

• See :ref:minimal_cost_complexity_pruning for details on the pruning process.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns

• ccp_path : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

• ccp_alphas : ndarray Effective alphas of subtree during pruning.

• impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path

method decision_path

val decision_path :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [`ArrayLike|`Object|`Spmatrix] Np.Obj.t

Return the decision path in the tree.

.. versionadded:: 0.18

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit

method fit

val fit :
  ?sample_weight:Py.Object.t ->
  ?check_input:Py.Object.t ->
  ?x_idx_sorted:Py.Object.t ->
  x:Py.Object.t ->
  y:Py.Object.t ->
  [> tag] Obj.t ->
  t

get_depth

method get_depth

val get_depth :
  [> tag] Obj.t ->
  Py.Object.t

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

• self.tree_.max_depth : int The maximum depth of the tree.

get_n_leaves

method get_n_leaves

val get_n_leaves :
  [> tag] Obj.t ->
  Py.Object.t

Return the number of leaves of the decision tree.

Returns

• self.tree_.n_leaves : int Number of leaves.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

• deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any Parameter names mapped to their values.

predict

method predict

val predict :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• **params : dict Estimator parameters.

Returns

• self : object Estimator instance.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

DecisionTreeClassifier

Module Sklearn.​Tree.​DecisionTreeClassifier wraps Python class sklearn.tree.DecisionTreeClassifier.

type t

create

constructor and attributes create

val create :
  ?criterion:[`Gini | `Entropy] ->
  ?splitter:[`Best | `Random] ->
  ?max_depth:int ->
  ?min_samples_split:[`F of float | `I of int] ->
  ?min_samples_leaf:[`F of float | `I of int] ->
  ?min_weight_fraction_leaf:float ->
  ?max_features:[`I of int | `Sqrt | `Auto | `F of float | `Log2] ->
  ?random_state:int ->
  ?max_leaf_nodes:int ->
  ?min_impurity_decrease:float ->
  ?min_impurity_split:float ->
  ?class_weight:[`Balanced | `DictIntToFloat of (int * float) list | `List_of_dict of Py.Object.t] ->
  ?presort:Py.Object.t ->
  ?ccp_alpha:float ->
  unit ->
  t

A decision tree classifier.

Read more in the :ref:User Guide <tree>.

Parameters

• criterion : {'gini', 'entropy'}, default='gini' The function to measure the quality of a split. Supported criteria are 'gini' for the Gini impurity and 'entropy' for the information gain.

• splitter : {'best', 'random'}, default='best' The strategy used to choose the split at each node. Supported strategies are 'best' to choose the best split and 'random' to choose the best random split.

• max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

• min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node:

  • If int, then consider min_samples_split as the minimum number.
  • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • If int, then consider min_samples_leaf as the minimum number.
  • If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• max_features : int, float or {'auto', 'sqrt', 'log2'}, default=None The number of features to consider when looking for the best split:

  - If int, then consider `max_features` features at each split.
  - If float, then `max_features` is a fraction and
    `int(max_features * n_features)` features are considered at each
    split.
  - If 'auto', then `max_features=sqrt(n_features)`.
  - If 'sqrt', then `max_features=sqrt(n_features)`.
  - If 'log2', then `max_features=log2(n_features)`.
  - If None, then `max_features=n_features`.
  

• Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

• random_state : int, RandomState instance, default=None Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to 'best'. When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer.

• See :term:Glossary <random_state> for details.

• max_leaf_nodes : int, default=None Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  The weighted impurity decrease equation is the following::

  N_t / N * (impurity - N_t_R / N_t * right_impurity
                      - N_t_L / N_t * left_impurity)
  

  where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

  N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  .. versionadded:: 0.19

• min_impurity_split : float, default=0 Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  .. deprecated:: 0.19 min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.

• class_weight : dict, list of dict or 'balanced', default=None Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

  Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

  The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

  For multi-output, the weights of each column of y will be multiplied.

  Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

• presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24.

  .. deprecated:: 0.22

• ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See :ref:minimal_cost_complexity_pruning for details.

  .. versionadded:: 0.22

Attributes

• classes_ : ndarray of shape (n_classes,) or list of ndarray The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

• feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_.

• Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:sklearn.inspection.permutation_importance as an alternative.

• max_features_ : int The inferred value of max_features.

• n_classes_ : int or list of int The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).

• n_features_ : int The number of features when fit is performed.

• n_outputs_ : int The number of outputs when fit is performed.

• tree_ : Tree The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.

See Also

• DecisionTreeRegressor : A decision tree regressor.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

References

.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning

.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, 'Classification and Regression Trees', Wadsworth, Belmont, CA, 1984.

.. [3] T. Hastie, R. Tibshirani and J. Friedman. 'Elements of Statistical Learning', Springer, 2009.

.. [4] L. Breiman, and A. Cutler, 'Random Forests',

• https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()
>>> cross_val_score(clf, iris.data, iris.target, cv=10)
...                             # doctest: +SKIP
...
array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,
        0.93...,  0.93...,  1.     ,  0.93...,  1.      ])

apply

method apply

val apply :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the index of the leaf that each sample is predicted as.

.. versionadded:: 0.17

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

cost_complexity_pruning_path

method cost_complexity_pruning_path

val cost_complexity_pruning_path :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Compute the pruning path during Minimal Cost-Complexity Pruning.

• See :ref:minimal_cost_complexity_pruning for details on the pruning process.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns

• ccp_path : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

• ccp_alphas : ndarray Effective alphas of subtree during pruning.

• impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path

method decision_path

val decision_path :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [`ArrayLike|`Object|`Spmatrix] Np.Obj.t

Return the decision path in the tree.

.. versionadded:: 0.18

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?check_input:bool ->
  ?x_idx_sorted:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Build a decision tree classifier from the training set (X, y).

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

• X_idx_sorted : array-like of shape (n_samples, n_features), default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do.

Returns

• self : DecisionTreeClassifier Fitted estimator.

get_depth

method get_depth

val get_depth :
  [> tag] Obj.t ->
  Py.Object.t

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

• self.tree_.max_depth : int The maximum depth of the tree.

get_n_leaves

method get_n_leaves

val get_n_leaves :
  [> tag] Obj.t ->
  Py.Object.t

Return the number of leaves of the decision tree.

Returns

• self.tree_.n_leaves : int Number of leaves.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

• deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any Parameter names mapped to their values.

predict

method predict

val predict :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values.

predict_log_proba

method predict_log_proba

val predict_log_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  Py.Object.t

Predict class log-probabilities of the input samples X.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

Returns

• proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:classes_.

predict_proba

method predict_proba

val predict_proba :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:classes_.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

• X : array-like of shape (n_samples, n_features) Test samples.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

• score : float Mean accuracy of self.predict(X) wrt. y.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• **params : dict Estimator parameters.

Returns

• self : object Estimator instance.

classes_

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

feature_importances_

attribute feature_importances_

val feature_importances_ : t -> [>`ArrayLike] Np.Obj.t
val feature_importances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

warning

attribute warning

val warning : t -> Py.Object.t
val warning_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

max_features_

attribute max_features_

val max_features_ : t -> int
val max_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_classes_

attribute n_classes_

val n_classes_ : t -> Py.Object.t
val n_classes_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_features_

attribute n_features_

val n_features_ : t -> int
val n_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_outputs_

attribute n_outputs_

val n_outputs_ : t -> int
val n_outputs_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

tree_

attribute tree_

val tree_ : t -> Py.Object.t
val tree_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

DecisionTreeRegressor

Module Sklearn.​Tree.​DecisionTreeRegressor wraps Python class sklearn.tree.DecisionTreeRegressor.

type t

create

constructor and attributes create

val create :
  ?criterion:[`Mse | `Friedman_mse | `Mae] ->
  ?splitter:[`Best | `Random] ->
  ?max_depth:int ->
  ?min_samples_split:[`F of float | `I of int] ->
  ?min_samples_leaf:[`F of float | `I of int] ->
  ?min_weight_fraction_leaf:float ->
  ?max_features:[`I of int | `Sqrt | `Auto | `F of float | `Log2] ->
  ?random_state:int ->
  ?max_leaf_nodes:int ->
  ?min_impurity_decrease:float ->
  ?min_impurity_split:float ->
  ?presort:Py.Object.t ->
  ?ccp_alpha:float ->
  unit ->
  t

A decision tree regressor.

Read more in the :ref:User Guide <tree>.

Parameters

• criterion : {'mse', 'friedman_mse', 'mae'}, default='mse' The function to measure the quality of a split. Supported criteria are 'mse' for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, 'friedman_mse', which uses mean squared error with Friedman's improvement score for potential splits, and 'mae' for the mean absolute error, which minimizes the L1 loss using the median of each terminal node.

  .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion.

• splitter : {'best', 'random'}, default='best' The strategy used to choose the split at each node. Supported strategies are 'best' to choose the best split and 'random' to choose the best random split.

• max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

• min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node:

  • If int, then consider min_samples_split as the minimum number.
  • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • If int, then consider min_samples_leaf as the minimum number.
  • If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• max_features : int, float or {'auto', 'sqrt', 'log2'}, default=None The number of features to consider when looking for the best split:

  • If int, then consider max_features features at each split.
  • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
  • If 'auto', then max_features=n_features.
  • If 'sqrt', then max_features=sqrt(n_features).
  • If 'log2', then max_features=log2(n_features).
  • If None, then max_features=n_features.

• Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

• random_state : int, RandomState instance, default=None Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to 'best'. When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer.

• See :term:Glossary <random_state> for details.

• max_leaf_nodes : int, default=None Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  The weighted impurity decrease equation is the following::

  N_t / N * (impurity - N_t_R / N_t * right_impurity
                      - N_t_L / N_t * left_impurity)
  

  where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

  N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  .. versionadded:: 0.19

• min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  .. deprecated:: 0.19 min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.

• presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24.

  .. deprecated:: 0.22

• ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See :ref:minimal_cost_complexity_pruning for details.

  .. versionadded:: 0.22

Attributes

• feature_importances_ : ndarray of shape (n_features,) The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_.

• Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:sklearn.inspection.permutation_importance as an alternative.

• max_features_ : int The inferred value of max_features.

• n_features_ : int The number of features when fit is performed.

• n_outputs_ : int The number of outputs when fit is performed.

• tree_ : Tree The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.

See Also

• DecisionTreeClassifier : A decision tree classifier.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

References

.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning

.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, 'Classification and Regression Trees', Wadsworth, Belmont, CA, 1984.

.. [3] T. Hastie, R. Tibshirani and J. Friedman. 'Elements of Statistical Learning', Springer, 2009.

.. [4] L. Breiman, and A. Cutler, 'Random Forests',

• https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = DecisionTreeRegressor(random_state=0)
>>> cross_val_score(regressor, X, y, cv=10)
...                    # doctest: +SKIP
...
array([-0.39..., -0.46...,  0.02...,  0.06..., -0.50...,
       0.16...,  0.11..., -0.73..., -0.30..., -0.00...])

apply

method apply

val apply :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the index of the leaf that each sample is predicted as.

.. versionadded:: 0.17

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

cost_complexity_pruning_path

method cost_complexity_pruning_path

val cost_complexity_pruning_path :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Compute the pruning path during Minimal Cost-Complexity Pruning.

• See :ref:minimal_cost_complexity_pruning for details on the pruning process.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns

• ccp_path : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

• ccp_alphas : ndarray Effective alphas of subtree during pruning.

• impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path

method decision_path

val decision_path :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [`ArrayLike|`Object|`Spmatrix] Np.Obj.t

Return the decision path in the tree.

.. versionadded:: 0.18

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?check_input:bool ->
  ?x_idx_sorted:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Build a decision tree regressor from the training set (X, y).

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

• X_idx_sorted : array-like of shape (n_samples, n_features), default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do.

Returns

• self : DecisionTreeRegressor Fitted estimator.

get_depth

method get_depth

val get_depth :
  [> tag] Obj.t ->
  Py.Object.t

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

• self.tree_.max_depth : int The maximum depth of the tree.

get_n_leaves

method get_n_leaves

val get_n_leaves :
  [> tag] Obj.t ->
  Py.Object.t

Return the number of leaves of the decision tree.

Returns

• self.tree_.n_leaves : int Number of leaves.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

• deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any Parameter names mapped to their values.

predict

method predict

val predict :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters

• X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

• score : float R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of :func:~sklearn.metrics.r2_score. This influences the score method of all the multioutput regressors (except for :class:~sklearn.multioutput.MultiOutputRegressor).

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• **params : dict Estimator parameters.

Returns

• self : object Estimator instance.

feature_importances_

attribute feature_importances_

val feature_importances_ : t -> [>`ArrayLike] Np.Obj.t
val feature_importances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

warning

attribute warning

val warning : t -> Py.Object.t
val warning_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

max_features_

attribute max_features_

val max_features_ : t -> int
val max_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_features_

attribute n_features_

val n_features_ : t -> int
val n_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_outputs_

attribute n_outputs_

val n_outputs_ : t -> int
val n_outputs_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

tree_

attribute tree_

val tree_ : t -> Py.Object.t
val tree_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

ExtraTreeClassifier

Module Sklearn.​Tree.​ExtraTreeClassifier wraps Python class sklearn.tree.ExtraTreeClassifier.

type t

create

constructor and attributes create

val create :
  ?criterion:[`Gini | `Entropy] ->
  ?splitter:[`Random | `Best] ->
  ?max_depth:int ->
  ?min_samples_split:[`F of float | `I of int] ->
  ?min_samples_leaf:[`F of float | `I of int] ->
  ?min_weight_fraction_leaf:float ->
  ?max_features:[`I of int | `Sqrt | `F of float | `PyObject of Py.Object.t | `None] ->
  ?random_state:int ->
  ?max_leaf_nodes:int ->
  ?min_impurity_decrease:float ->
  ?min_impurity_split:float ->
  ?class_weight:[`Balanced | `DictIntToFloat of (int * float) list | `List_of_dict of Py.Object.t] ->
  ?ccp_alpha:float ->
  unit ->
  t

An extremely randomized tree classifier.

Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.

• Warning: Extra-trees should only be used within ensemble methods.

Read more in the :ref:User Guide <tree>.

Parameters

• criterion : {'gini', 'entropy'}, default='gini' The function to measure the quality of a split. Supported criteria are 'gini' for the Gini impurity and 'entropy' for the information gain.

• splitter : {'random', 'best'}, default='random' The strategy used to choose the split at each node. Supported strategies are 'best' to choose the best split and 'random' to choose the best random split.

• max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

• min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node:

  • If int, then consider min_samples_split as the minimum number.
  • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • If int, then consider min_samples_leaf as the minimum number.
  • If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• max_features : int, float, {'auto', 'sqrt', 'log2'} or None, default='auto' The number of features to consider when looking for the best split:

  - If int, then consider `max_features` features at each split.
  - If float, then `max_features` is a fraction and
    `int(max_features * n_features)` features are considered at each
    split.
  - If 'auto', then `max_features=sqrt(n_features)`.
  - If 'sqrt', then `max_features=sqrt(n_features)`.
  - If 'log2', then `max_features=log2(n_features)`.
  - If None, then `max_features=n_features`.
  

• Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

• random_state : int, RandomState instance, default=None Used to pick randomly the max_features used at each split.

• See :term:Glossary <random_state> for details.

• max_leaf_nodes : int, default=None Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  The weighted impurity decrease equation is the following::

  N_t / N * (impurity - N_t_R / N_t * right_impurity
                      - N_t_L / N_t * left_impurity)
  

  where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

  N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  .. versionadded:: 0.19

• min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  .. deprecated:: 0.19 min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.

• class_weight : dict, list of dict or 'balanced', default=None Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

  Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

  The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

  For multi-output, the weights of each column of y will be multiplied.

  Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

• ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See :ref:minimal_cost_complexity_pruning for details.

  .. versionadded:: 0.22

Attributes

• classes_ : ndarray of shape (n_classes,) or list of ndarray The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

• max_features_ : int The inferred value of max_features.

• n_classes_ : int or list of int The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).

• feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

• Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:sklearn.inspection.permutation_importance as an alternative.

• n_features_ : int The number of features when fit is performed.

• n_outputs_ : int The number of outputs when fit is performed.

• tree_ : Tree The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.

See Also

• ExtraTreeRegressor : An extremely randomized tree regressor.

• sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier.

• sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

References

.. [1] P. Geurts, D. Ernst., and L. Wehenkel, 'Extremely randomized trees', Machine Learning, 63(1), 3-42, 2006.

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import BaggingClassifier
>>> from sklearn.tree import ExtraTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
...    X, y, random_state=0)
>>> extra_tree = ExtraTreeClassifier(random_state=0)
>>> cls = BaggingClassifier(extra_tree, random_state=0).fit(
...    X_train, y_train)
>>> cls.score(X_test, y_test)
0.8947...

apply

method apply

val apply :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the index of the leaf that each sample is predicted as.

.. versionadded:: 0.17

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

cost_complexity_pruning_path

method cost_complexity_pruning_path

val cost_complexity_pruning_path :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Compute the pruning path during Minimal Cost-Complexity Pruning.

• See :ref:minimal_cost_complexity_pruning for details on the pruning process.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns

• ccp_path : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

• ccp_alphas : ndarray Effective alphas of subtree during pruning.

• impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path

method decision_path

val decision_path :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [`ArrayLike|`Object|`Spmatrix] Np.Obj.t

Return the decision path in the tree.

.. versionadded:: 0.18

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?check_input:bool ->
  ?x_idx_sorted:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Build a decision tree classifier from the training set (X, y).

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

• X_idx_sorted : array-like of shape (n_samples, n_features), default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do.

Returns

• self : DecisionTreeClassifier Fitted estimator.

get_depth

method get_depth

val get_depth :
  [> tag] Obj.t ->
  Py.Object.t

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

• self.tree_.max_depth : int The maximum depth of the tree.

get_n_leaves

method get_n_leaves

val get_n_leaves :
  [> tag] Obj.t ->
  Py.Object.t

Return the number of leaves of the decision tree.

Returns

• self.tree_.n_leaves : int Number of leaves.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

• deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any Parameter names mapped to their values.

predict

method predict

val predict :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values.

predict_log_proba

method predict_log_proba

val predict_log_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  Py.Object.t

Predict class log-probabilities of the input samples X.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

Returns

• proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:classes_.

predict_proba

method predict_proba

val predict_proba :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:classes_.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

• X : array-like of shape (n_samples, n_features) Test samples.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

• score : float Mean accuracy of self.predict(X) wrt. y.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• **params : dict Estimator parameters.

Returns

• self : object Estimator instance.

classes_

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

max_features_

attribute max_features_

val max_features_ : t -> int
val max_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_classes_

attribute n_classes_

val n_classes_ : t -> Py.Object.t
val n_classes_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

feature_importances_

attribute feature_importances_

val feature_importances_ : t -> [>`ArrayLike] Np.Obj.t
val feature_importances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

warning

attribute warning

val warning : t -> Py.Object.t
val warning_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_features_

attribute n_features_

val n_features_ : t -> int
val n_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_outputs_

attribute n_outputs_

val n_outputs_ : t -> int
val n_outputs_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

tree_

attribute tree_

val tree_ : t -> Py.Object.t
val tree_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

ExtraTreeRegressor

Module Sklearn.​Tree.​ExtraTreeRegressor wraps Python class sklearn.tree.ExtraTreeRegressor.

type t

create

constructor and attributes create

val create :
  ?criterion:[`Mse | `Friedman_mse | `Mae] ->
  ?splitter:[`Random | `Best] ->
  ?max_depth:int ->
  ?min_samples_split:[`F of float | `I of int] ->
  ?min_samples_leaf:[`F of float | `I of int] ->
  ?min_weight_fraction_leaf:float ->
  ?max_features:[`I of int | `Sqrt | `F of float | `PyObject of Py.Object.t | `None] ->
  ?random_state:int ->
  ?min_impurity_decrease:float ->
  ?min_impurity_split:float ->
  ?max_leaf_nodes:int ->
  ?ccp_alpha:float ->
  unit ->
  t

An extremely randomized tree regressor.

Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.

• Warning: Extra-trees should only be used within ensemble methods.

Read more in the :ref:User Guide <tree>.

Parameters

• criterion : {'mse', 'friedman_mse', 'mae'}, default='mse' The function to measure the quality of a split. Supported criteria are 'mse' for the mean squared error, which is equal to variance reduction as feature selection criterion, and 'mae' for the mean absolute error.

  .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion.

• splitter : {'random', 'best'}, default='random' The strategy used to choose the split at each node. Supported strategies are 'best' to choose the best split and 'random' to choose the best random split.

• max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

• min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node:

  • If int, then consider min_samples_split as the minimum number.
  • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • If int, then consider min_samples_leaf as the minimum number.
  • If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  .. versionchanged:: 0.18 Added float values for fractions.

• min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• max_features : int, float, {'auto', 'sqrt', 'log2'} or None, default='auto' The number of features to consider when looking for the best split:

  • If int, then consider max_features features at each split.
  • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
  • If 'auto', then max_features=n_features.
  • If 'sqrt', then max_features=sqrt(n_features).
  • If 'log2', then max_features=log2(n_features).
  • If None, then max_features=n_features.

• Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

• random_state : int, RandomState instance, default=None Used to pick randomly the max_features used at each split.

• See :term:Glossary <random_state> for details.

• min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  The weighted impurity decrease equation is the following::

  N_t / N * (impurity - N_t_R / N_t * right_impurity
                      - N_t_L / N_t * left_impurity)
  

  where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

  N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  .. versionadded:: 0.19

• min_impurity_split : float, (default=0) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  .. deprecated:: 0.19 min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.

• max_leaf_nodes : int, default=None Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See :ref:minimal_cost_complexity_pruning for details.

  .. versionadded:: 0.22

Attributes

• max_features_ : int The inferred value of max_features.

• n_features_ : int The number of features when fit is performed.

• feature_importances_ : ndarray of shape (n_features,) Return impurity-based feature importances (the higher, the more important the feature).

• Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:sklearn.inspection.permutation_importance as an alternative.

• n_outputs_ : int The number of outputs when fit is performed.

• tree_ : Tree The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.

See Also

• ExtraTreeClassifier : An extremely randomized tree classifier.

• sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier.

• sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

References

.. [1] P. Geurts, D. Ernst., and L. Wehenkel, 'Extremely randomized trees', Machine Learning, 63(1), 3-42, 2006.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import BaggingRegressor
>>> from sklearn.tree import ExtraTreeRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> extra_tree = ExtraTreeRegressor(random_state=0)
>>> reg = BaggingRegressor(extra_tree, random_state=0).fit(
...     X_train, y_train)
>>> reg.score(X_test, y_test)
0.33...

apply

method apply

val apply :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the index of the leaf that each sample is predicted as.

.. versionadded:: 0.17

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• X_leaves : array-like of shape (n_samples,) For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

cost_complexity_pruning_path

method cost_complexity_pruning_path

val cost_complexity_pruning_path :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Compute the pruning path during Minimal Cost-Complexity Pruning.

• See :ref:minimal_cost_complexity_pruning for details on the pruning process.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns

• ccp_path : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

• ccp_alphas : ndarray Effective alphas of subtree during pruning.

• impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path

method decision_path

val decision_path :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [`ArrayLike|`Object|`Spmatrix] Np.Obj.t

Return the decision path in the tree.

.. versionadded:: 0.18

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?check_input:bool ->
  ?x_idx_sorted:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Build a decision tree regressor from the training set (X, y).

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

• X_idx_sorted : array-like of shape (n_samples, n_features), default=None The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do.

Returns

• self : DecisionTreeRegressor Fitted estimator.

get_depth

method get_depth

val get_depth :
  [> tag] Obj.t ->
  Py.Object.t

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

• self.tree_.max_depth : int The maximum depth of the tree.

get_n_leaves

method get_n_leaves

val get_n_leaves :
  [> tag] Obj.t ->
  Py.Object.t

Return the number of leaves of the decision tree.

Returns

• self.tree_.n_leaves : int Number of leaves.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

• deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

• params : mapping of string to any Parameter names mapped to their values.

predict

method predict

val predict :
  ?check_input:bool ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

• check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do.

Returns

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters

• X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

• sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

• score : float R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of :func:~sklearn.metrics.r2_score. This influences the score method of all the multioutput regressors (except for :class:~sklearn.multioutput.MultiOutputRegressor).

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• **params : dict Estimator parameters.

Returns

• self : object Estimator instance.

max_features_

attribute max_features_

val max_features_ : t -> int
val max_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_features_

attribute n_features_

val n_features_ : t -> int
val n_features_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

feature_importances_

attribute feature_importances_

val feature_importances_ : t -> [>`ArrayLike] Np.Obj.t
val feature_importances_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

warning

attribute warning

val warning : t -> Py.Object.t
val warning_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_outputs_

attribute n_outputs_

val n_outputs_ : t -> int
val n_outputs_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

tree_

attribute tree_

val tree_ : t -> Py.Object.t
val tree_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

export_graphviz

function export_graphviz

val export_graphviz :
  ?out_file:[`S of string | `File_object of Py.Object.t] ->
  ?max_depth:int ->
  ?feature_names:string list ->
  ?class_names:[`StringList of string list | `Bool of bool] ->
  ?label:[`All | `Root | `None] ->
  ?filled:bool ->
  ?leaves_parallel:bool ->
  ?impurity:bool ->
  ?node_ids:bool ->
  ?proportion:bool ->
  ?rotate:bool ->
  ?rounded:bool ->
  ?special_characters:bool ->
  ?precision:int ->
  decision_tree:[>`DecisionTreeClassifier] Np.Obj.t ->
  unit ->
  string option

Export a decision tree in DOT format.

This function generates a GraphViz representation of the decision tree, which is then written into out_file. Once exported, graphical renderings can be generated using, for example::

$ dot -Tps tree.dot -o tree.ps      (PostScript format)
$ dot -Tpng tree.dot -o tree.png    (PNG format)

The sample counts that are shown are weighted with any sample_weights that might be present.

Read more in the :ref:User Guide <tree>.

Parameters

• decision_tree : decision tree classifier The decision tree to be exported to GraphViz.

• out_file : file object or string, optional (default=None) Handle or name of the output file. If None, the result is returned as a string.

  .. versionchanged:: 0.20 Default of out_file changed from 'tree.dot' to None.

• max_depth : int, optional (default=None) The maximum depth of the representation. If None, the tree is fully generated.

• feature_names : list of strings, optional (default=None) Names of each of the features.

• class_names : list of strings, bool or None, optional (default=None) Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output. If True, shows a symbolic representation of the class name.

• label : {'all', 'root', 'none'}, optional (default='all') Whether to show informative labels for impurity, etc. Options include 'all' to show at every node, 'root' to show only at the top root node, or 'none' to not show at any node.

• filled : bool, optional (default=False) When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.

• leaves_parallel : bool, optional (default=False) When set to True, draw all leaf nodes at the bottom of the tree.

• impurity : bool, optional (default=True) When set to True, show the impurity at each node.

• node_ids : bool, optional (default=False) When set to True, show the ID number on each node.

• proportion : bool, optional (default=False) When set to True, change the display of 'values' and/or 'samples' to be proportions and percentages respectively.

• rotate : bool, optional (default=False) When set to True, orient tree left to right rather than top-down.

• rounded : bool, optional (default=False) When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.

• special_characters : bool, optional (default=False) When set to False, ignore special characters for PostScript compatibility.

• precision : int, optional (default=3) Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.

Returns

• dot_data : string String representation of the input tree in GraphViz dot format. Only returned if out_file is None.

  .. versionadded:: 0.18

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn import tree

>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()

>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf)
'digraph Tree {...

export_text

function export_text

val export_text :
  ?feature_names:string list ->
  ?max_depth:int ->
  ?spacing:int ->
  ?decimals:int ->
  ?show_weights:bool ->
  decision_tree:[>`BaseDecisionTree] Np.Obj.t ->
  unit ->
  string

Build a text report showing the rules of a decision tree.

Note that backwards compatibility may not be supported.

Parameters

• decision_tree : object The decision tree estimator to be exported. It can be an instance of DecisionTreeClassifier or DecisionTreeRegressor.

• feature_names : list, optional (default=None) A list of length n_features containing the feature names. If None generic names will be used ('feature_0', 'feature_1', ...).

• max_depth : int, optional (default=10) Only the first max_depth levels of the tree are exported. Truncated branches will be marked with '...'.

• spacing : int, optional (default=3) Number of spaces between edges. The higher it is, the wider the result.

• decimals : int, optional (default=2) Number of decimal digits to display.

• show_weights : bool, optional (default=False) If true the classification weights will be exported on each leaf. The classification weights are the number of samples each class.

Returns

• report : string Text summary of all the rules in the decision tree.

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.tree import export_text
>>> iris = load_iris()
>>> X = iris['data']
>>> y = iris['target']
>>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
>>> decision_tree = decision_tree.fit(X, y)
>>> r = export_text(decision_tree, feature_names=iris['feature_names'])
>>> print(r)
|--- petal width (cm) <= 0.80
|   |--- class: 0
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- class: 1
|   |--- petal width (cm) >  1.75
|   |   |--- class: 2

plot_tree

function plot_tree

val plot_tree :
  ?max_depth:int ->
  ?feature_names:string list ->
  ?class_names:[`StringList of string list | `Bool of bool] ->
  ?label:[`All | `Root | `None] ->
  ?filled:bool ->
  ?impurity:bool ->
  ?node_ids:bool ->
  ?proportion:bool ->
  ?rotate:bool ->
  ?rounded:bool ->
  ?precision:int ->
  ?ax:Py.Object.t ->
  ?fontsize:int ->
  decision_tree:[>`BaseDecisionTree] Np.Obj.t ->
  unit ->
  Py.Object.t

Plot a decision tree.

The sample counts that are shown are weighted with any sample_weights that might be present.

The visualization is fit automatically to the size of the axis. Use the figsize or dpi arguments of plt.figure to control the size of the rendering.

Read more in the :ref:User Guide <tree>.

.. versionadded:: 0.21

Parameters

• decision_tree : decision tree regressor or classifier The decision tree to be plotted.

• max_depth : int, optional (default=None) The maximum depth of the representation. If None, the tree is fully generated.

• feature_names : list of strings, optional (default=None) Names of each of the features.

• class_names : list of strings, bool or None, optional (default=None) Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output. If True, shows a symbolic representation of the class name.

• label : {'all', 'root', 'none'}, optional (default='all') Whether to show informative labels for impurity, etc. Options include 'all' to show at every node, 'root' to show only at the top root node, or 'none' to not show at any node.

• filled : bool, optional (default=False) When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.

• impurity : bool, optional (default=True) When set to True, show the impurity at each node.

• node_ids : bool, optional (default=False) When set to True, show the ID number on each node.

• proportion : bool, optional (default=False) When set to True, change the display of 'values' and/or 'samples' to be proportions and percentages respectively.

• rotate : bool, optional (default=False) This parameter has no effect on the matplotlib tree visualisation and it is kept here for backward compatibility.

  .. deprecated:: 0.23 rotate is deprecated in 0.23 and will be removed in 0.25.

• rounded : bool, optional (default=False) When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.

• precision : int, optional (default=3) Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.

• ax : matplotlib axis, optional (default=None) Axes to plot to. If None, use current axis. Any previous content is cleared.

• fontsize : int, optional (default=None) Size of text font. If None, determined automatically to fit figure.

Returns

• annotations : list of artists List containing the artists for the annotation boxes making up the tree.

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn import tree

>>> clf = tree.DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()

>>> clf = clf.fit(iris.data, iris.target)
>>> tree.plot_tree(clf)  # doctest: +SKIP
[Text(251.5,345.217,'X[3] <= 0.8...