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 : 
  ?base_estimator:Py.Object.t ->
  ?n_estimators:int ->
  ?learning_rate:float ->
  ?algorithm:[ `SAMME | `SAMME_R ] ->
  ?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
  unit ->
  t

An AdaBoost classifier.

An AdaBoost 1 classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

This class implements the algorithm known as AdaBoost-SAMME 2.

Read more in the :ref:`User Guide <adaboost>`.

.. versionadded:: 0.14

Parameters ---------- base_estimator : object, optional (default=None) The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper ``classes_`` and ``n_classes_`` attributes. If ``None``, then the base estimator is ``DecisionTreeClassifier(max_depth=1)``.

n_estimators : int, optional (default=50) The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, optional (default=1.) Learning rate shrinks the contribution of each classifier by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators``.

algorithm : 'SAMME', 'SAMME.R', optional (default='SAMME.R') If 'SAMME.R' then use the SAMME.R real boosting algorithm. ``base_estimator`` must support calculation of class probabilities. If 'SAMME' then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.

random_state : int, RandomState instance or None, optional (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`.

Attributes ---------- base_estimator_ : estimator The base estimator from which the ensemble is grown.

estimators_ : list of classifiers The collection of fitted sub-estimators.

classes_ : array of shape (n_classes,) The classes labels.

n_classes_ : int The number of classes.

estimator_weights_ : array of floats Weights for each estimator in the boosted ensemble.

estimator_errors_ : array of floats Classification error for each estimator in the boosted ensemble.

feature_importances_ : ndarray of shape (n_features,) The feature importances if supported by the ``base_estimator``.

See Also -------- AdaBoostRegressor An AdaBoost regressor that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction.

GradientBoostingClassifier GB builds an additive model in a forward stage-wise fashion. Regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.

sklearn.tree.DecisionTreeClassifier A non-parametric supervised learning method used for classification. Creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

References ---------- .. 1 Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting", 1995.

.. 2 J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.

Examples -------- >>> from sklearn.ensemble import AdaBoostClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=1000, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = AdaBoostClassifier(n_estimators=100, random_state=0) >>> clf.fit(X, y) AdaBoostClassifier(n_estimators=100, random_state=0) >>> clf.feature_importances_ array(0.28..., 0.42..., 0.14..., 0.16...) >>> clf.predict([0, 0, 0, 0]) array(1) >>> clf.score(X, y) 0.983...

val get_item : index:Py.Object.t -> t -> Py.Object.t

Return the index'th estimator in the ensemble.

val decision_function : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Ndarray.t

Compute the decision function of ``X``.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns ------- score : array, shape = n_samples, k The decision function of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute. Binary classification is a special cases with ``k == 1``, otherwise ``k==n_classes``. For binary classification, values closer to -1 or 1 mean more like the first or second class in ``classes_``, respectively.

val fit : 
  ?sample_weight:Ndarray.t ->
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  y:Ndarray.t ->
  t ->
  t

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

y : array-like of shape (n_samples,) The target values (class labels).

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, the sample weights are initialized to ``1 / n_samples``.

Returns ------- self : object Fitted estimator.

val get_params : ?deep:bool -> t -> Py.Object.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 : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Ndarray.t

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Returns ------- y : ndarray of shape (n_samples,) The predicted classes.

val predict_log_proba : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Ndarray.t

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Returns ------- p : array of shape (n_samples, n_classes) The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

val predict_proba : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Ndarray.t

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

Returns ------- p : array of shape (n_samples, n_classes) The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

val score : 
  ?sample_weight:Ndarray.t ->
  x:Ndarray.t ->
  y:Ndarray.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 staged_decision_function : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Py.Object.t

Compute decision function of ``X`` for each boosting iteration.

This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

Yields ------ score : generator of array, shape = n_samples, k The decision function of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute. Binary classification is a special cases with ``k == 1``, otherwise ``k==n_classes``. For binary classification, values closer to -1 or 1 mean more like the first or second class in ``classes_``, respectively.

val staged_predict : x:Ndarray.t -> t -> Py.Object.t

Return staged predictions for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields ------ y : generator of array, shape = n_samples The predicted classes.

val staged_predict_proba : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Py.Object.t

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities on a test set after each boost.

Yields ------- p : generator of array, shape = n_samples The class probabilities of the input samples. The order of outputs is the same of that of the :term:`classes_` attribute.

val staged_score : 
  ?sample_weight:Ndarray.t ->
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  y:Ndarray.t ->
  t ->
  Py.Object.t

Return staged scores for X, y.

This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

y : array-like of shape (n_samples,) Labels for X.

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

Yields ------ z : float

val base_estimator_ : t -> Py.Object.t

Attribute base_estimator_: see constructor for documentation

val estimators_ : t -> Py.Object.t

Attribute estimators_: see constructor for documentation

val classes_ : t -> Ndarray.t

Attribute classes_: see constructor for documentation

val n_classes_ : t -> int

Attribute n_classes_: see constructor for documentation

val estimator_weights_ : t -> Ndarray.t

Attribute estimator_weights_: see constructor for documentation

val estimator_errors_ : t -> Ndarray.t

Attribute estimator_errors_: see constructor for documentation

val feature_importances_ : t -> Ndarray.t

Attribute feature_importances_: see constructor for documentation

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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.