ExtraTreesRegressor

An extra-trees regressor.

This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

Constructors

constructor()

Signature

new ExtraTreesRegressor(opts?: object): ExtraTreesRegressor;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.bootstrap?`	`boolean`	Whether bootstrap samples are used when building trees. If `false`, the whole dataset is used to build each tree. `Default Value` `false`
`opts.ccp_alpha?`	`any`	Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than `ccp\_alpha` will be chosen. By default, no pruning is performed. See Minimal Cost-Complexity Pruning for details. `Default Value` `0`
`opts.criterion?`	`"squared_error"` \| `"absolute_error"` \| `"friedman_mse"` \| `"poisson"`	The function to measure the quality of a split. Supported criteria are “squared_error” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, “friedman_mse”, which uses mean squared error with Friedman’s improvement score for potential splits, “absolute_error” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and “poisson” which uses reduction in Poisson deviance to find splits. Training using “absolute_error” is significantly slower than when using “squared_error”. `Default Value` `'squared_error'`
`opts.max_depth?`	`number`	The maximum depth of the tree. If `undefined`, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
`opts.max_features?`	`number` \| `"sqrt"` \| `"log2"`	The number of features to consider when looking for the best split: `Default Value` `1`
`opts.max_leaf_nodes?`	`number`	Grow trees with `max\_leaf\_nodes` in best-first fashion. Best nodes are defined as relative reduction in impurity. If `undefined` then unlimited number of leaf nodes.
`opts.max_samples?`	`number`	If bootstrap is `true`, the number of samples to draw from X to train each base estimator.
`opts.min_impurity_decrease?`	`number`	A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following: `Default Value` `0`
`opts.min_samples_leaf?`	`number`	The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least `min\_samples\_leaf` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. `Default Value` `1`
`opts.min_samples_split?`	`number`	The minimum number of samples required to split an internal node: `Default Value` `2`
`opts.min_weight_fraction_leaf?`	`number`	The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. `Default Value` `0`
`opts.n_estimators?`	`number`	The number of trees in the forest. `Default Value` `100`
`opts.n_jobs?`	`number`	The number of jobs to run in parallel. `fit`, `predict`, `decision\_path` and `apply` are all parallelized over the trees. `undefined` means 1 unless in a `joblib.parallel\_backend` 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 score. By default, `r2\_score` is used. Provide a callable with signature `metric(y\_true, y\_pred)` to use a custom metric. Only available if `bootstrap=True`. `Default Value` `false`
`opts.random_state?`	`number`	Controls 3 sources of randomness:
`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 forest. See Glossary and Fitting additional weak-learners for details. `Default Value` `false`

Returns

ExtraTreesRegressor

Defined in: generated/ensemble/ExtraTreesRegressor.ts:25

Methods

apply()

Apply trees in the forest to X, return leaf indices.

Signature

apply(opts: object): Promise<ArrayLike[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csr\_matrix`.

Returns

Promise<ArrayLike[]>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:237

decision_path()

Return the decision path in the forest.

Signature

decision_path(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csr\_matrix`.

Returns

Promise<any[]>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:272

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/ExtraTreesRegressor.ts:220

fit()

Build a forest of trees 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. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csc\_matrix`.
`opts.sample_weight?`	`ArrayLike`	Sample weights. If `undefined`, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
`opts.y?`	`ArrayLike`	The target values (class labels in classification, real numbers in regression).

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:309

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:360

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/ExtraTreesRegressor.ts:154

predict()

Predict regression target for X.

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

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csr\_matrix`.

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:400

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/ExtraTreesRegressor.ts:437

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `fit`.

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:490

set_score_request()

Request metadata passed to the score method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_score_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `score`.

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:532

Properties

_isDisposed

boolean = false

Defined in: generated/ensemble/ExtraTreesRegressor.ts:23

_isInitialized

boolean = false

Defined in: generated/ensemble/ExtraTreesRegressor.ts:22

_py

PythonBridge

Defined in: generated/ensemble/ExtraTreesRegressor.ts:21

id

string

Defined in: generated/ensemble/ExtraTreesRegressor.ts:18

opts

any

Defined in: generated/ensemble/ExtraTreesRegressor.ts:19

Accessors

estimator_

The child estimator template used to create the collection of fitted sub-estimators.

Signature

estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:570

estimators_

The collection of fitted sub-estimators.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:597

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/ExtraTreesRegressor.ts:651

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:624

n_outputs_

The number of outputs.

Signature

n_outputs_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:678

oob_prediction_

Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob\_score is true.

Signature

oob_prediction_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/ExtraTreesRegressor.ts:732

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/ExtraTreesRegressor.ts:705

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/ensemble/ExtraTreesRegressor.ts:141

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/ensemble/ExtraTreesRegressor.ts:145

Files

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ExtraTreesRegressor

Constructors

constructor()

Signature

Parameters

Returns

Methods

apply()

Signature

Parameters

Returns

decision_path()

Signature

Parameters

Returns

dispose()

Signature

Returns

fit()

Signature

Parameters

Returns

get_metadata_routing()

Signature

Parameters

Returns

init()

Signature

Parameters

Returns

predict()

Signature

Parameters

Returns

score()

Signature

Parameters

Returns

set_fit_request()

Signature

Parameters

Returns

set_score_request()

Signature

Parameters

Returns

Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

estimator_

Signature

Returns

estimators_

Signature

Returns

feature_names_in_

Signature

Returns

n_features_in_

Signature

Returns

n_outputs_

Signature

Returns

oob_prediction_

Signature

Returns

oob_score_

Signature

Returns