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 ->
  ?loss:[ `Linear | `Square | `Exponential ] ->
  ?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
  unit ->
  t

An AdaBoost regressor.

An AdaBoost 1 regressor is a meta-estimator 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. As such, subsequent regressors focus more on difficult cases.

This class implements the algorithm known as AdaBoost.R2 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. If ``None``, then the base estimator is ``DecisionTreeRegressor(max_depth=3)``.

n_estimators : integer, 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 regressor by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators``.

loss : 'linear', 'square', 'exponential', optional (default='linear') The loss function to use when updating the weights after each boosting iteration.

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.

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

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

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

Examples -------- >>> from sklearn.ensemble import AdaBoostRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=4, n_informative=2, ... random_state=0, shuffle=False) >>> regr = AdaBoostRegressor(random_state=0, n_estimators=100) >>> regr.fit(X, y) AdaBoostRegressor(n_estimators=100, random_state=0) >>> regr.feature_importances_ array(0.2788..., 0.7109..., 0.0065..., 0.0036...) >>> regr.predict([0, 0, 0, 0]) array(4.7972...) >>> regr.score(X, y) 0.9771...

See also -------- AdaBoostClassifier, GradientBoostingRegressor, sklearn.tree.DecisionTreeRegressor

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

.. 2 H. Drucker, "Improving Regressors using Boosting Techniques", 1997.

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

Return the index'th estimator in the ensemble.

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

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

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.

y : array-like of shape (n_samples,) The target values (real numbers).

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

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 regression value for X.

The predicted regression value of an input sample is computed as the weighted median prediction of the classifiers in the ensemble.

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

val score : 
  ?sample_weight:Ndarray.t ->
  x:Ndarray.t ->
  y:Ndarray.t ->
  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 will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).

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

Return staged predictions for X.

The predicted regression value of an input sample is computed as the weighted median 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, sparse matrix of shape (n_samples, n_features) The training input samples.

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

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 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.