DecisionTreeClassifier

A decision tree classifier.

Constructors

constructor()

Signature

new DecisionTreeClassifier(opts?: object): DecisionTreeClassifier;

Parameters

Name	Type	Description
`opts?`	`object`	-
`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 `undefined`, 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))` 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. `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` \| `"auto"` \| `"sqrt"` \| `"log2"`	The number of features to consider when looking for the best split:
`opts.max_leaf_nodes?`	`number`	Grow a tree 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.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.random_state?`	`number`	Controls the randomness of the estimator. The features are always randomly permuted at each split, even if `splitter` is set to `"best"`. When `max\_features < n\_features`, the algorithm will select `max\_features` at random at each split before finding the best split among them. But the best found split may vary across different runs, even if `max\_features=n\_features`. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, `random\_state` has to be fixed to an integer. See Glossary for details.
`opts.splitter?`	`"random"` \| `"best"`	The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split. `Default Value` `'best'`

Returns

DecisionTreeClassifier

Defined in: generated/tree/DecisionTreeClassifier.ts:23

Methods

apply()

Return the index of the leaf that each sample is predicted as.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. `Default Value` `true`

Returns

Promise<ArrayLike>

Defined in: generated/tree/DecisionTreeClassifier.ts:204

cost_complexity_pruning_path()

Compute the pruning path during Minimal Cost-Complexity Pruning.

See Minimal Cost-Complexity Pruning for details on the pruning process.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The training input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to 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. 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) as integers or strings.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:250

decision_path()

Return the decision path in the tree.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. `Default Value` `true`

Returns

Promise<any[]>

Defined in: generated/tree/DecisionTreeClassifier.ts:302

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/tree/DecisionTreeClassifier.ts:187

fit()

Build a decision tree classifier 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, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc\_matrix`.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. `Default Value` `true`
`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. 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) as integers or strings.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:349

get_depth()

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Signature

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

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:409

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/tree/DecisionTreeClassifier.ts:441

get_n_leaves()

Return the number of leaves of the decision tree.

Signature

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

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:479

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/tree/DecisionTreeClassifier.ts:125

predict()

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. `Default Value` `true`

Returns

Promise<ArrayLike>

Defined in: generated/tree/DecisionTreeClassifier.ts:511

predict_log_proba()

Predict class log-probabilities of the input samples X.

Signature

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

Parameters

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

Returns

Promise<ArrayLike[]>

Defined in: generated/tree/DecisionTreeClassifier.ts:557

predict_proba()

Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.
`opts.check_input?`	`boolean`	Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. `Default Value` `true`

Returns

Promise<ArrayLike[]>

Defined in: generated/tree/DecisionTreeClassifier.ts:597

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/tree/DecisionTreeClassifier.ts:646

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.check_input?`	`string` \| `boolean`	Metadata routing for `check\_input` parameter in `fit`.
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `fit`.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:699

set_predict_proba_request()

Request metadata passed to the predict\_proba 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_predict_proba_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.check_input?`	`string` \| `boolean`	Metadata routing for `check\_input` parameter in `predict\_proba`.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:746

set_predict_request()

Request metadata passed to the predict 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_predict_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.check_input?`	`string` \| `boolean`	Metadata routing for `check\_input` parameter in `predict`.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:788

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/tree/DecisionTreeClassifier.ts:830

Properties

_isDisposed

boolean = false

Defined in: generated/tree/DecisionTreeClassifier.ts:21

_isInitialized

boolean = false

Defined in: generated/tree/DecisionTreeClassifier.ts:20

_py

PythonBridge

Defined in: generated/tree/DecisionTreeClassifier.ts:19

id

string

Defined in: generated/tree/DecisionTreeClassifier.ts:16

opts

any

Defined in: generated/tree/DecisionTreeClassifier.ts:17

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/tree/DecisionTreeClassifier.ts:868

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/tree/DecisionTreeClassifier.ts:976

max_features_

The inferred value of max_features.

Signature

max_features_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeClassifier.ts:895

n_classes_

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

Signature

n_classes_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeClassifier.ts:922

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeClassifier.ts:949

n_outputs_

The number of outputs when fit is performed.

Signature

n_outputs_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeClassifier.ts:1003

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/tree/DecisionTreeClassifier.ts:112

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/tree/DecisionTreeClassifier.ts:116

tree_

The underlying Tree object. Please refer to help(sklearn.tree.\_tree.Tree) for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes.

Signature

tree_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeClassifier.ts:1030

Files

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DecisionTreeClassifier

Constructors

constructor()

Signature

Parameters

Returns

Methods

apply()

Signature

Parameters

Returns

cost_complexity_pruning_path()

Signature

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Returns

decision_path()

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dispose()

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fit()

Signature

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get_depth()

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get_metadata_routing()

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get_n_leaves()

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init()

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predict()

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predict_log_proba()

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predict_proba()

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score()

Signature

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set_fit_request()

Signature

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set_predict_proba_request()

Signature

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set_predict_request()

Signature

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set_score_request()

Signature

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Returns