package sklearn

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sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?criterion:[ `Gini | `Entropy ] ->
  ?splitter:[ `Random | `Best ] ->
  ?max_depth:int ->
  ?min_samples_split:[ `I of int | `F of float ] ->
  ?min_samples_leaf:[ `I of int | `F of float ] ->
  ?min_weight_fraction_leaf:float ->
  ?max_features:[ `I of int | `F of float | `Auto | `Sqrt | `Log2 | `None ] ->
  ?random_state:int ->
  ?max_leaf_nodes:int ->
  ?min_impurity_decrease:float ->
  ?min_impurity_split:float ->
  ?class_weight:
    [ `DictIntToFloat of (int * float) list
    | `List_of_dict of Py.Object.t
    | `Balanced ] ->
  ?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 or RandomState, default=None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.

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=1e-7) 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`` will change 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,) Return the feature importances (the higher, the more important the feature).

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.

val apply : ?check_input:bool -> x:Arr.t -> t -> Arr.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.

val cost_complexity_pruning_path : 
  ?sample_weight:Arr.t ->
  x:Arr.t ->
  y:Arr.t ->
  t ->
  Py.Object.t * Arr.t * Arr.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 : Bunch Dictionary-like object, with 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``.

val decision_path : ?check_input:bool -> x:Arr.t -> t -> Csr_matrix.t

Return the decision path in the tree.

.. versionadded:: 0.18

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.

val fit : 
  ?sample_weight:Arr.t ->
  ?check_input:bool ->
  ?x_idx_sorted:Arr.t ->
  x:Arr.t ->
  y:Arr.t ->
  t ->
  t

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

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

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.

val get_depth : 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.

val get_n_leaves : t -> Py.Object.t

Return the number of leaves of the decision tree.

Returns ------- self.tree_.n_leaves : int Number of leaves.

val get_params : ?deep:bool -> 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.

val predict : ?check_input:bool -> x:Arr.t -> t -> Arr.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.

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.

val predict_log_proba : x:Arr.t -> t -> Py.Object.t

Predict class log-probabilities of the input samples X.

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_`.

val predict_proba : ?check_input:bool -> x:Arr.t -> t -> Arr.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.

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_`.

val score : ?sample_weight:Arr.t -> x:Arr.t -> y:Arr.t -> 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.

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

val classes_ : t -> Arr.t

Attribute classes_: get value or raise Not_found if None.

val classes_opt : t -> Arr.t option

Attribute classes_: get value as an option.

val max_features_ : t -> int

Attribute max_features_: get value or raise Not_found if None.

val max_features_opt : t -> int option

Attribute max_features_: get value as an option.

val n_classes_ : t -> Py.Object.t

Attribute n_classes_: get value or raise Not_found if None.

val n_classes_opt : t -> Py.Object.t option

Attribute n_classes_: get value as an option.

val feature_importances_ : t -> Arr.t

Attribute feature_importances_: get value or raise Not_found if None.

val feature_importances_opt : t -> Arr.t option

Attribute feature_importances_: get value as an option.

val n_features_ : t -> int

Attribute n_features_: get value or raise Not_found if None.

val n_features_opt : t -> int option

Attribute n_features_: get value as an option.

val n_outputs_ : t -> int

Attribute n_outputs_: get value or raise Not_found if None.

val n_outputs_opt : t -> int option

Attribute n_outputs_: get value as an option.

val tree_ : t -> Py.Object.t

Attribute tree_: get value or raise Not_found if None.

val tree_opt : t -> Py.Object.t option

Attribute tree_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

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

Pretty-print the object to a formatter.