Class: RandomForestRegressor

A random forest regressor.

A random forest is a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. Trees in the forest use the best split strategy, i.e. equivalent to passing splitter="best" to the underlying DecisionTreeRegressor. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

For a comparison between tree-based ensemble models see the example Comparing Random Forests and Histogram Gradient Boosting models.

Constructors

new RandomForestRegressor()

new RandomForestRegressor(opts?): RandomForestRegressor

Parameters

Parameter	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.
`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.
`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”.
`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:
`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:
`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.
`opts.min_samples_split`?	`number`	The minimum number of samples required to split an internal node:
`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.
`opts.monotonic_cst`?	`any`[]	1: monotonically increasing
`opts.n_estimators`?	`number`	The number of trees in the forest.
`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`.
`opts.random_state`?	`number`	Controls both the randomness of the bootstrapping of the samples used when building trees (if `bootstrap=True`) and the sampling of the features to consider when looking for the best split at each node (if `max_features < n_features`). See Glossary for details.
`opts.verbose`?	`number`	Controls the verbosity when fitting and predicting.
`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 trees for details.

Returns RandomForestRegressor

Defined in generated/ensemble/RandomForestRegressor.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/ensemble/RandomForestRegressor.ts:25
`_isInitialized`	`boolean`	`false`	generated/ensemble/RandomForestRegressor.ts:24
`_py`	`PythonBridge`	`undefined`	generated/ensemble/RandomForestRegressor.ts:23
`id`	`string`	`undefined`	generated/ensemble/RandomForestRegressor.ts:20
`opts`	`any`	`undefined`	generated/ensemble/RandomForestRegressor.ts:21

Accessors

estimator_

Get Signature

get estimator_(): Promise<any>

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

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:535

estimators_

Get Signature

get estimators_(): Promise<any>

The collection of fitted sub-estimators.

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:562

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/ensemble/RandomForestRegressor.ts:616

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/ensemble/RandomForestRegressor.ts:589

n_outputs_

Get Signature

get n_outputs_(): Promise<number>

The number of outputs when fit is performed.

Returns Promise<number>

Defined in generated/ensemble/RandomForestRegressor.ts:643

oob_prediction_

Get Signature

get oob_prediction_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/ensemble/RandomForestRegressor.ts:697

oob_score_

Get Signature

get oob_score_(): Promise<number>

Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is true.

Returns Promise<number>

Defined in generated/ensemble/RandomForestRegressor.ts:670

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/ensemble/RandomForestRegressor.ts:148

Methods

apply()

apply(opts): Promise<ArrayLike[]>

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

Parameters

Parameter	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/RandomForestRegressor.ts:221

decision_path()

decision_path(opts): Promise<any[]>

Return the decision path in the forest.

Parameters

Parameter	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/RandomForestRegressor.ts:255

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/ensemble/RandomForestRegressor.ts:204

fit()

fit(opts): Promise<any>

Build a forest of trees from the training set (X, y).

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.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.y`?	`ArrayLike`	The target values (class labels in classification, real numbers in regression).

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:291

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

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

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:337

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/ensemble/RandomForestRegressor.ts:161

predict()

predict(opts): Promise<ArrayLike>

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.

Parameters

Parameter	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/RandomForestRegressor.ts:375

score()

score(opts): Promise<number>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`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.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/ensemble/RandomForestRegressor.ts:411

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

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

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:459

set_score_request()

set_score_request(opts): Promise<any>

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:

Parameters

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

Returns Promise<any>

Defined in generated/ensemble/RandomForestRegressor.ts:499

RandomForestClassifier RandomizedSearchCV