Class: RandomForestClassifier

A random forest classifier.

A random forest is a meta estimator that fits a number of decision tree classifiers 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 RandomForestClassifier()

new RandomForestClassifier(opts?): RandomForestClassifier

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.class_weight`?	`any`	Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes \* np.bincount(y))` The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
`opts.criterion`?	`"gini"` \| `"entropy"` \| `"log_loss"`	The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical formulation. Note: This parameter is tree-specific.
`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: monotonic increase
`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, `accuracy_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 RandomForestClassifier

Defined in generated/ensemble/RandomForestClassifier.ts:27

Properties

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

Accessors

classes_

Get Signature

get classes_(): Promise<ArrayLike>

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

Returns Promise<ArrayLike>

Defined in generated/ensemble/RandomForestClassifier.ts:682

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/RandomForestClassifier.ts:628

estimators_

Get Signature

get estimators_(): Promise<any>

The collection of fitted sub-estimators.

Returns Promise<any>

Defined in generated/ensemble/RandomForestClassifier.ts:655

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/RandomForestClassifier.ts:763

n_classes_

Get Signature

get n_classes_(): Promise<number | any[]>

The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).

Returns Promise<number | any[]>

Defined in generated/ensemble/RandomForestClassifier.ts:709

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/ensemble/RandomForestClassifier.ts:736

n_outputs_

Get Signature

get n_outputs_(): Promise<number>

The number of outputs when fit is performed.

Returns Promise<number>

Defined in generated/ensemble/RandomForestClassifier.ts:790

oob_decision_function_

Get Signature

get oob_decision_function_(): Promise<ArrayLike[]>

Decision function 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_decision_function_ might contain NaN. This attribute exists only when oob_score is true.

Returns Promise<ArrayLike[]>

Defined in generated/ensemble/RandomForestClassifier.ts:844

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/RandomForestClassifier.ts:817

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/RandomForestClassifier.ts:163

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/RandomForestClassifier.ts:236

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/RandomForestClassifier.ts:270

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/RandomForestClassifier.ts:219

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/RandomForestClassifier.ts:306

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/RandomForestClassifier.ts:352

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/RandomForestClassifier.ts:176

predict()

predict(opts): Promise<ArrayLike>

Predict class for X.

The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.

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/RandomForestClassifier.ts:390

predict_log_proba()

predict_log_proba(opts): Promise<any>

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities 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<any>

Defined in generated/ensemble/RandomForestClassifier.ts:428

predict_proba()

predict_proba(opts): Promise<any>

Predict class probabilities for X.

The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.

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/RandomForestClassifier.ts:466

score()

score(opts): Promise<number>

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples.
`opts.y`?	`ArrayLike`	True labels for `X`.

Returns Promise<number>

Defined in generated/ensemble/RandomForestClassifier.ts:504

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/RandomForestClassifier.ts:552

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/RandomForestClassifier.ts:592

RadiusNeighborsTransformer RandomForestRegressor