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
| 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.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? | 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
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
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
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
| 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/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
| 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/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
| 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/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
| 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. |
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: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
| Name | Type |
|---|---|
opts | object |
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
| Name | Type |
|---|---|
opts | object |
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
| Name | Type |
|---|---|
py | PythonBridge |
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
| 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/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
| Name | Type | Description |
|---|---|---|
opts | object | - |
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? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/tree/DecisionTreeRegressor.ts:502 (opens in a new tab)