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 User Guide.
Added in version 0.15.
The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a DecisionTreeRegressor.
Added in version 1.2: base_estimator was renamed to estimator.
The number of base estimators in the ensemble.
The number of samples to draw from X to train each base estimator (with replacement by default, see bootstrap for more details).
If int, then draw max_samples samples.
If float, then draw max_samples * X.shape[0] samples.
The number of features to draw from X to train each base estimator ( without replacement by default, see bootstrap_features for more details).
If int, then draw max_features features.
If float, then draw max(1, int(max_features * n_features_in_)) features.
Whether samples are drawn with replacement. If False, sampling without replacement is performed. If fitting with sample_weight, it is strongly recommended to choose True, as only drawing with replacement will ensure the expected frequency semantics of sample_weight.
Whether features are drawn with replacement.
Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True.
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 the Glossary.
The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See Glossary.
Controls the verbosity when fitting and predicting.
The base estimator from which the ensemble is grown.
Added in version 1.2: base_estimator_ was renamed to estimator_.
Number of features seen during fit.
Added in version 0.24.
n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
Added in version 1.0.
The collection of fitted sub-estimators.
estimators_samples_list of arrays
The subset of drawn samples for each base estimator.
The subset of drawn features for each base estimator.
Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.
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.
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.
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(estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([[0, 0, 0, 0]]) array([-2.8720])
Build a Bagging ensemble of estimators from the training set (X, y).
The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
The target values (class labels in classification, real numbers in regression).
Sample weights. If None, then samples are equally weighted. Used as probabilities to sample the training set. Note that the expected frequency semantics for the sample_weight parameter are only fulfilled when sampling with replacement bootstrap=True.
Parameters to pass to the underlying estimators.
Added in version 1.5: Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Fitted estimator.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.5.
A MetadataRouter encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
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.
The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Parameters routed to the predict method of the sub-estimators via the metadata routing API.
Added in version 1.7: Only available if sklearn.set_config(enable_metadata_routing=True) is set. See Metadata Routing User Guide for more details.
The predicted values.
Return coefficient of determination on test data.
The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), 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.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) w.r.t. y.
Notes
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Configure whether metadata should be requested to be passed to the fit method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tofitif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it tofit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.The default (
sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Metadata routing for sample_weight parameter in fit.
The updated object.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Configure whether metadata should be requested to be passed to the score method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it toscore.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.The default (
sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Metadata routing for sample_weight parameter in score.
The updated object.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4