GradientBoostingRegressor
Gradient Boosting for regression.
This estimator builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.
sklearn.ensemble.HistGradientBoostingRegressor is a much faster variant of this algorithm for intermediate datasets (n\_samples >= 10\_000).
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new GradientBoostingRegressor(opts?: object): GradientBoostingRegressor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | The alpha-quantile of the huber loss function and the quantile loss function. Only if loss='huber' or loss='quantile'. Values must be in the range (0.0, 1.0). Default Value 0.9 |
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. Values must be in the range \0.0, inf). See [Minimal Cost-Complexity Pruning for details. Default Value 0 |
opts.criterion? | "squared_error" | "friedman_mse" | The function to measure the quality of a split. Supported criteria are “friedman_mse” for the mean squared error with improvement score by Friedman, “squared_error” for mean squared error. The default value of “friedman_mse” is generally the best as it can provide a better approximation in some cases. Default Value 'friedman_mse' |
opts.init? | "zero" | An estimator object that is used to compute the initial predictions. init has to provide fit and predict. If ‘zero’, the initial raw predictions are set to zero. By default a DummyEstimator is used, predicting either the average target value (for loss=’squared_error’), or a quantile for the other losses. |
opts.learning_rate? | number | Learning rate shrinks the contribution of each tree by learning\_rate. There is a trade-off between learning_rate and n_estimators. Values must be in the range \[0.0, inf). Default Value 0.1 |
opts.loss? | "quantile" | "squared_error" | "absolute_error" | "huber" | Loss function to be optimized. ‘squared_error’ refers to the squared error for regression. ‘absolute_error’ refers to the absolute error of regression and is a robust loss function. ‘huber’ is a combination of the two. ‘quantile’ allows quantile regression (use alpha to specify the quantile). Default Value 'squared_error' |
opts.max_depth? | number | Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. If undefined, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. If int, values must be in the range \[1, inf). Default Value 3 |
opts.max_features? | number | "sqrt" | "log2" | The number of features to consider when looking for the best split: |
opts.max_leaf_nodes? | number | Grow trees with max\_leaf\_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. Values must be in the range \[2, inf). 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. Values must be in the range \[0.0, inf). 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. Values must be in the range \[0.0, 0.5\]. Default Value 0 |
opts.n_estimators? | number | The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. Values must be in the range \1, inf). Default Value 100 |
opts.n_iter_no_change? | number | n\_iter\_no\_change is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to undefined to disable early stopping. If set to a number, it will set aside validation\_fraction size of the training data as validation and terminate training when validation score is not improving in all of the previous n\_iter\_no\_change numbers of iterations. Values must be in the range \[1, inf). |
opts.random_state? | number | Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if n\_iter\_no\_change is not undefined. Pass an int for reproducible output across multiple function calls. See [Glossary. |
opts.subsample? | number | The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n\_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias. Values must be in the range (0.0, 1.0\]. Default Value 1 |
opts.tol? | number | Tolerance for the early stopping. When the loss is not improving by at least tol for n\_iter\_no\_change iterations (if set to a number), the training stops. Values must be in the range \0.0, inf). Default Value 0.0001 |
opts.validation_fraction? | number | The proportion of training data to set aside as validation set for early stopping. Values must be in the range (0.0, 1.0). Only used if n\_iter\_no\_change is set to an integer. Default Value 0.1 |
opts.verbose? | number | Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. Values must be in the range \[0, inf). Default Value 0 |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. See [the Glossary. Default Value false |
Returns
Defined in: generated/ensemble/GradientBoostingRegressor.ts:27 (opens in a new tab)
Methods
apply()
Apply trees in the ensemble to X, return leaf indices.
Signature
apply(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted to a sparse csr\_matrix. |
Returns
Promise<ArrayLike[]>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:275 (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/ensemble/GradientBoostingRegressor.ts:258 (opens in a new tab)
fit()
Fit the gradient boosting model.
Signature
fit(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.monitor? | any | The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of \_fit\_stages as keyword arguments callable(i, self, locals()). If the callable returns true the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshoting. |
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. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. |
opts.y? | ArrayLike | Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes. |
Returns
Promise<any>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:312 (opens in a new tab)
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/ensemble/GradientBoostingRegressor.ts:370 (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/ensemble/GradientBoostingRegressor.ts:184 (opens in a new tab)
predict()
Predict regression target for X.
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. |
Returns
Promise<ArrayLike>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:408 (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/ensemble/GradientBoostingRegressor.ts:447 (opens in a new tab)
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.monitor? | string | boolean | Metadata routing for monitor parameter in fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:502 (opens in a new tab)
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/ensemble/GradientBoostingRegressor.ts:549 (opens in a new tab)
staged_predict()
Predict regression target at each stage for X.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Signature
staged_predict(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. |
Returns
Promise<any[]>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:589 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/ensemble/GradientBoostingRegressor.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/ensemble/GradientBoostingRegressor.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/ensemble/GradientBoostingRegressor.ts:23 (opens in a new tab)
id
string
Defined in: generated/ensemble/GradientBoostingRegressor.ts:20 (opens in a new tab)
opts
any
Defined in: generated/ensemble/GradientBoostingRegressor.ts:21 (opens in a new tab)
Accessors
estimators_
The collection of fitted sub-estimators.
Signature
estimators_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:762 (opens in a new tab)
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/ensemble/GradientBoostingRegressor.ts:843 (opens in a new tab)
init_
The estimator that provides the initial predictions. Set via the init argument or loss.init\_estimator.
Signature
init_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:735 (opens in a new tab)
max_features_
The inferred value of max_features.
Signature
max_features_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:870 (opens in a new tab)
n_estimators_
The number of estimators as selected by early stopping (if n\_iter\_no\_change is specified). Otherwise it is set to n\_estimators.
Signature
n_estimators_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:789 (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/ensemble/GradientBoostingRegressor.ts:816 (opens in a new tab)
oob_improvement_
The improvement in loss on the out-of-bag samples relative to the previous iteration. oob\_improvement\_\[0\] is the improvement in loss of the first stage over the init estimator. Only available if subsample < 1.0.
Signature
oob_improvement_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:627 (opens in a new tab)
oob_score_
The last value of the loss on the out-of-bag samples. It is the same as oob\_scores\_\[-1\]. Only available if subsample < 1.0.
Signature
oob_score_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:681 (opens in a new tab)
oob_scores_
The full history of the loss values on the out-of-bag samples. Only available if subsample < 1.0.
Signature
oob_scores_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:654 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/ensemble/GradientBoostingRegressor.ts:171 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/ensemble/GradientBoostingRegressor.ts:175 (opens in a new tab)
train_score_
The i-th score train\_score\_\[i\] is the loss of the model at iteration i on the in-bag sample. If subsample \== 1 this is the loss on the training data.
Signature
train_score_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/GradientBoostingRegressor.ts:708 (opens in a new tab)