BaggingRegressor
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new BaggingRegressor(opts?: object): BaggingRegressor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.base_estimator? | any | Use estimator instead. Default Value 'deprecated' |
opts.bootstrap? | boolean | Whether samples are drawn with replacement. If false, sampling without replacement is performed. Default Value true |
opts.bootstrap_features? | boolean | Whether features are drawn with replacement. Default Value false |
opts.estimator? | any | The base estimator to fit on random subsets of the dataset. If undefined, then the base estimator is a DecisionTreeRegressor. |
opts.max_features? | number | The number of features to draw from X to train each base estimator ( without replacement by default, see bootstrap\_features for more details). Default Value 1 |
opts.max_samples? | number | The number of samples to draw from X to train each base estimator (with replacement by default, see bootstrap for more details). Default Value 1 |
opts.n_estimators? | number | The number of base estimators in the ensemble. Default Value 10 |
opts.n_jobs? | number | The number of jobs to run in parallel for both fit and predict. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.oob_score? | boolean | Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=true. Default Value false |
opts.random_state? | number | 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. |
opts.verbose? | number | Controls the verbosity when fitting and predicting. Default Value 0 |
opts.warm_start? | boolean | 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. Default Value false |
Returns
Defined in: generated/ensemble/BaggingRegressor.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/ensemble/BaggingRegressor.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/ensemble/BaggingRegressor.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/ensemble/BaggingRegressor.ts:23 (opens in a new tab)
id
string
Defined in: generated/ensemble/BaggingRegressor.ts:20 (opens in a new tab)
opts
any
Defined in: generated/ensemble/BaggingRegressor.ts:21 (opens in a new tab)
Accessors
estimator_
The base estimator from which the ensemble is grown.
Signature
estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/BaggingRegressor.ts:335 (opens in a new tab)
estimators_
The collection of fitted sub-estimators.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/BaggingRegressor.ts:416 (opens in a new tab)
estimators_features_
The subset of drawn features for each base estimator.
Signature
estimators_features_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/BaggingRegressor.ts:443 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/BaggingRegressor.ts:389 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/BaggingRegressor.ts:362 (opens in a new tab)
oob_prediction_
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.
Signature
oob_prediction_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/BaggingRegressor.ts:497 (opens in a new tab)
oob_score_
Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob\_score is true.
Signature
oob_score_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/BaggingRegressor.ts:470 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/ensemble/BaggingRegressor.ts:110 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/ensemble/BaggingRegressor.ts:114 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/ensemble/BaggingRegressor.ts:181 (opens in a new tab)
fit()
Build a Bagging ensemble of estimators from the training set (X, y).
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting. |
opts.y? | ArrayLike | The target values (class labels in classification, real numbers in regression). |
Returns
Promise<any>
Defined in: generated/ensemble/BaggingRegressor.ts:198 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/ensemble/BaggingRegressor.ts:123 (opens in a new tab)
predict()
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.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/ensemble/BaggingRegressor.ts:249 (opens in a new tab)
score()
Return the coefficient of determination of the prediction.
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | 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. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/ensemble/BaggingRegressor.ts:286 (opens in a new tab)