Class: ExtraTreeRegressor
An extremely randomized tree regressor.
Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.
Warning: Extra-trees should only be used within ensemble methods.
Read more in the User Guide.
Constructors
new ExtraTreeRegressor()
new ExtraTreeRegressor(
opts?):ExtraTreeRegressor
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 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 | 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 | Used to pick randomly the max_features used at each split. 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 ExtraTreeRegressor
Defined in generated/tree/ExtraTreeRegressor.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/tree/ExtraTreeRegressor.ts:25 |
_isInitialized | boolean | false | generated/tree/ExtraTreeRegressor.ts:24 |
_py | PythonBridge | undefined | generated/tree/ExtraTreeRegressor.ts:23 |
id | string | undefined | generated/tree/ExtraTreeRegressor.ts:20 |
opts | any | undefined | generated/tree/ExtraTreeRegressor.ts:21 |
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/ExtraTreeRegressor.ts:732
max_features_
Get Signature
get max_features_():
Promise<number>
The inferred value of max_features.
Returns Promise<number>
Defined in generated/tree/ExtraTreeRegressor.ts:678
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/tree/ExtraTreeRegressor.ts:705
n_outputs_
Get Signature
get n_outputs_():
Promise<number>
The number of outputs when fit is performed.
Returns Promise<number>
Defined in generated/tree/ExtraTreeRegressor.ts:759
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/ExtraTreeRegressor.ts:110
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/ExtraTreeRegressor.ts:786
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/ExtraTreeRegressor.ts:183
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/ExtraTreeRegressor.ts:226
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/ExtraTreeRegressor.ts:272
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/ExtraTreeRegressor.ts:166
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/ExtraTreeRegressor.ts:315
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/ExtraTreeRegressor.ts:368
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/ExtraTreeRegressor.ts:398
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/ExtraTreeRegressor.ts:434
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/ExtraTreeRegressor.ts:123
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/ExtraTreeRegressor.ts:466
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/ExtraTreeRegressor.ts:509
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/ExtraTreeRegressor.ts:557
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/ExtraTreeRegressor.ts:602
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/ExtraTreeRegressor.ts:642