Class: DecisionTreeRegressor
A decision tree regressor.
Read more in the User Guide.
Constructors
new DecisionTreeRegressor()
new DecisionTreeRegressor(
opts?):DecisionTreeRegressor
Parameters
| Parameter | 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. |
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 the half mean Poisson deviance to find splits. |
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 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: |
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.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. |
Returns DecisionTreeRegressor
Defined in generated/tree/DecisionTreeRegressor.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/tree/DecisionTreeRegressor.ts:21 |
_isInitialized | boolean | false | generated/tree/DecisionTreeRegressor.ts:20 |
_py | PythonBridge | undefined | generated/tree/DecisionTreeRegressor.ts:19 |
id | string | undefined | generated/tree/DecisionTreeRegressor.ts:16 |
opts | any | undefined | generated/tree/DecisionTreeRegressor.ts:17 |
Accessors
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/tree/DecisionTreeRegressor.ts:728
max_features_
Get Signature
get max_features_():
Promise<number>
The inferred value of max_features.
Returns Promise<number>
Defined in generated/tree/DecisionTreeRegressor.ts:674
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/tree/DecisionTreeRegressor.ts:701
n_outputs_
Get Signature
get n_outputs_():
Promise<number>
The number of outputs when fit is performed.
Returns Promise<number>
Defined in generated/tree/DecisionTreeRegressor.ts:755
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/tree/DecisionTreeRegressor.ts:104
tree_
Get Signature
get tree_():
Promise<any>
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.
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:782
Methods
apply()
apply(
opts):Promise<ArrayLike>
Return the index of the leaf that each sample is predicted as.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | boolean | Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. |
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/DecisionTreeRegressor.ts:177
cost_complexity_pruning_path()
cost_complexity_pruning_path(
opts):Promise<any>
Compute the pruning path during Minimal Cost-Complexity Pruning.
See Minimal Cost-Complexity Pruning for details on the pruning process.
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. 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, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix. |
opts.y? | ArrayLike | The target values (class labels) as integers or strings. |
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:220
decision_path()
decision_path(
opts):Promise<any[]>
Return the decision path in the tree.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | boolean | Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. |
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<any[]>
Defined in generated/tree/DecisionTreeRegressor.ts:266
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/tree/DecisionTreeRegressor.ts:160
fit()
fit(
opts):Promise<any>
Build a decision tree regressor from the training set (X, y).
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | boolean | Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. |
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. |
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.y? | ArrayLike | The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency. |
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:309
get_depth()
get_depth(
opts):Promise<any>
Return the depth of the decision tree.
The depth of a tree is the maximum distance between the root and any leaf.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:362
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/tree/DecisionTreeRegressor.ts:394
get_n_leaves()
get_n_leaves(
opts):Promise<any>
Return the number of leaves of the decision tree.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:430
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/tree/DecisionTreeRegressor.ts:117
predict()
predict(
opts):Promise<ArrayLike>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | boolean | Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing. |
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/DecisionTreeRegressor.ts:462
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/tree/DecisionTreeRegressor.ts:505
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.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/DecisionTreeRegressor.ts:553
set_predict_request()
set_predict_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.check_input? | string | boolean | Metadata routing for check_input parameter in predict. |
Returns Promise<any>
Defined in generated/tree/DecisionTreeRegressor.ts:598
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/tree/DecisionTreeRegressor.ts:638