ExtraTreesClassifier

An extra-trees classifier.

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 ExtraTreesClassifier(opts?: object): ExtraTreesClassifier;

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.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. `Default Value` `'gini'`
`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` `'sqrt'`
`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, `accuracy\_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

ExtraTreesClassifier

Defined in: generated/ensemble/ExtraTreesClassifier.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/ExtraTreesClassifier.ts:254

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/ExtraTreesClassifier.ts:289

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/ExtraTreesClassifier.ts:237

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/ExtraTreesClassifier.ts:326

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/ExtraTreesClassifier.ts:377

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/ExtraTreesClassifier.ts:169

predict()

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.

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/ExtraTreesClassifier.ts:417

predict_log_proba()

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.

Signature

predict_log_proba(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/ExtraTreesClassifier.ts:454

predict_proba()

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.

Signature

predict_proba(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/ExtraTreesClassifier.ts:494

score()

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.

Signature

score(opts: object): Promise<number>;

Parameters

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

Returns

Promise<number>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:533

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/ExtraTreesClassifier.ts:586

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/ExtraTreesClassifier.ts:628

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

classes_

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

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:720

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/ExtraTreesClassifier.ts:666

estimators_

The collection of fitted sub-estimators.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:693

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/ExtraTreesClassifier.ts:801

n_classes_

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

Signature

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

Returns

Promise<number | any[]>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:747

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:774

n_outputs_

The number of outputs when fit is performed.

Signature

n_outputs_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:828

oob_decision_function_

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.

Signature

oob_decision_function_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/ensemble/ExtraTreesClassifier.ts:882

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/ExtraTreesClassifier.ts:855

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/ensemble/ExtraTreesClassifier.ts:156

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/ensemble/ExtraTreesClassifier.ts:160

Files

ExtraTreesClassifier.md

Latest commit

History

ExtraTreesClassifier.md

File metadata and controls

ExtraTreesClassifier

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

predict_log_proba()

Signature

Parameters

Returns

predict_proba()

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

classes_

Signature

Returns

estimator_

Signature

Returns

estimators_

Signature

Returns

feature_names_in_

Signature

Returns

n_classes_