package sklearn

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sklearn
- Sklearn

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Class type

type tag = [

| `BaggingRegressor

]

type t =
  [ `BaggingRegressor
  | `BaseBagging
  | `BaseEnsemble
  | `BaseEstimator
  | `MetaEstimatorMixin
  | `Object
  | `RegressorMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

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

val as_regressor : t -> [ `RegressorMixin ] Obj.t

val as_meta_estimator : t -> [ `MetaEstimatorMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_bagging : t -> [ `BaseBagging ] Obj.t

val as_ensemble : t -> [ `BaseEnsemble ] Obj.t

val create : 
  ?base_estimator:[> `BaseEstimator ] Np.Obj.t ->
  ?n_estimators:int ->
  ?max_samples:[ `F of float | `I of int ] ->
  ?max_features:[ `F of float | `I of int ] ->
  ?bootstrap:bool ->
  ?bootstrap_features:bool ->
  ?oob_score:bool ->
  ?warm_start:bool ->
  ?n_jobs:int ->
  ?random_state:int ->
  ?verbose:int ->
  unit ->
  t

A Bagging regressor.

A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.

This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting 1_. If samples are drawn with replacement, then the method is known as Bagging 2_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces 3_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches 4_.

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

.. versionadded:: 0.15

Parameters ---------- base_estimator : object or None, optional (default=None) The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.

n_estimators : int, optional (default=10) The number of base estimators in the ensemble.

max_samples : int or float, optional (default=1.0) The number of samples to draw from X to train each base estimator.

If int, then draw `max_samples` samples.
If float, then draw `max_samples * X.shape0` samples.

max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator.

If int, then draw `max_features` features.
If float, then draw `max_features * X.shape1` features.

bootstrap : boolean, optional (default=True) Whether samples are drawn with replacement. If False, sampling without replacement is performed.

bootstrap_features : boolean, optional (default=False) Whether features are drawn with replacement.

oob_score : bool Whether to use out-of-bag samples to estimate the generalization error.

warm_start : bool, optional (default=False) When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary <warm_start>`.

n_jobs : int or None, optional (default=None) The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

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`.

verbose : int, optional (default=0) Controls the verbosity when fitting and predicting.

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

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

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

estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected.

estimators_features_ : list of arrays The subset of drawn features for each base estimator.

oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True.

oob_prediction_ : ndarray of shape (n_samples,) Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_prediction_` might contain NaN. This attribute exists only when ``oob_score`` is True.

Examples -------- >>> from sklearn.svm import SVR >>> from sklearn.ensemble import BaggingRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=100, n_features=4, ... n_informative=2, n_targets=1, ... random_state=0, shuffle=False) >>> regr = BaggingRegressor(base_estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([0, 0, 0, 0]) array(-2.8720...)

References ----------

.. 1 L. Breiman, 'Pasting small votes for classification in large databases and on-line', Machine Learning, 36(1), 85-103, 1999.

.. 2 L. Breiman, 'Bagging predictors', Machine Learning, 24(2), 123-140, 1996.

.. 3 T. Ho, 'The random subspace method for constructing decision forests', Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.

.. 4 G. Louppe and P. Geurts, 'Ensembles on Random Patches', Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.

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

Return the index'th estimator in the ensemble.

val iter : [> tag ] Obj.t -> Dict.t Stdlib.Seq.t

Return iterator over estimators in the ensemble.

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

Build a Bagging ensemble of estimators from the training set (X, y).

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression).

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.

Returns ------- self : object

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.

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

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

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

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 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 -> [> 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.

val base_estimator_ : t -> [ `BaseEstimator | `Object ] Np.Obj.t

Attribute base_estimator_: get value or raise Not_found if None.

val base_estimator_opt : t -> [ `BaseEstimator | `Object ] Np.Obj.t option

Attribute base_estimator_: 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 estimators_ : t -> [ `BaseEstimator | `Object ] Np.Obj.t list

Attribute estimators_: get value or raise Not_found if None.

val estimators_opt : t -> [ `BaseEstimator | `Object ] Np.Obj.t list option

Attribute estimators_: get value as an option.

val estimators_samples_ : t -> Np.Numpy.Ndarray.List.t

Attribute estimators_samples_: get value or raise Not_found if None.

val estimators_samples_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute estimators_samples_: get value as an option.

val estimators_features_ : t -> Np.Numpy.Ndarray.List.t

Attribute estimators_features_: get value or raise Not_found if None.

val estimators_features_opt : t -> Np.Numpy.Ndarray.List.t option

Attribute estimators_features_: get value as an option.

val oob_score_ : t -> float

Attribute oob_score_: get value or raise Not_found if None.

val oob_score_opt : t -> float option

Attribute oob_score_: get value as an option.

val oob_prediction_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute oob_prediction_: get value or raise Not_found if None.

val oob_prediction_opt : t -> [> `ArrayLike ] Np.Obj.t option

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

Pretty-print the object to a formatter.