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Classes
DecisionTreeRegressor

DecisionTreeRegressor

A decision tree regressor.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new DecisionTreeRegressor(opts?: object): DecisionTreeRegressor;

Parameters

NameTypeDescription
opts?object-
opts.ccp_alpha?anyComplexity 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. Default Value 'squared_error'
opts.max_depth?numberThe 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?numberGrow 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?numberA 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?numberThe 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?numberThe minimum number of samples required to split an internal node: Default Value 2
opts.min_weight_fraction_leaf?numberThe 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?numberControls 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

DecisionTreeRegressor

Defined in: generated/tree/DecisionTreeRegressor.ts:23 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/tree/DecisionTreeRegressor.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/tree/DecisionTreeRegressor.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/tree/DecisionTreeRegressor.ts:19 (opens in a new tab)

id

string

Defined in: generated/tree/DecisionTreeRegressor.ts:16 (opens in a new tab)

opts

any

Defined in: generated/tree/DecisionTreeRegressor.ts:17 (opens in a new tab)

Accessors

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/DecisionTreeRegressor.ts:605 (opens in a new tab)

max_features_

The inferred value of max_features.

Signature

max_features_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeRegressor.ts:551 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeRegressor.ts:578 (opens in a new tab)

n_outputs_

The number of outputs when fit is performed.

Signature

n_outputs_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeRegressor.ts:632 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/tree/DecisionTreeRegressor.ts:99 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/tree/DecisionTreeRegressor.ts:103 (opens in a new tab)

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/DecisionTreeRegressor.ts:659 (opens in a new tab)

Methods

apply()

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

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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?booleanAllow 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/DecisionTreeRegressor.ts:189 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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?ArrayLikeSample 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?ArrayLikeThe target values (class labels) as integers or strings.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeRegressor.ts:235 (opens in a new tab)

decision_path()

Return the decision path in the tree.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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?booleanAllow 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/DecisionTreeRegressor.ts:287 (opens in a new tab)

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/DecisionTreeRegressor.ts:172 (opens in a new tab)

fit()

Build a decision tree regressor from the training set (X, y).

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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?booleanAllow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. Default Value true
opts.sample_weight?ArrayLikeSample 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.
opts.y?ArrayLikeThe target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeRegressor.ts:334 (opens in a new tab)

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

NameType
optsobject

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeRegressor.ts:394 (opens in a new tab)

get_n_leaves()

Return the number of leaves of the decision tree.

Signature

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

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/tree/DecisionTreeRegressor.ts:424 (opens in a new tab)

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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/tree/DecisionTreeRegressor.ts:112 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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?booleanAllow 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/DecisionTreeRegressor.ts:456 (opens in a new tab)

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

NameTypeDescription
optsobject-
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?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/tree/DecisionTreeRegressor.ts:502 (opens in a new tab)