Class: HistGradientBoostingRegressor
Histogram-based Gradient Boosting Regression Tree.
This estimator is much faster than GradientBoostingRegressor for big datasets (n_samples >= 10 000).
This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. See Features in Histogram Gradient Boosting Trees for a usecase example of this feature.
This implementation is inspired by LightGBM.
Read more in the User Guide.
Constructors
new HistGradientBoostingRegressor()
new HistGradientBoostingRegressor(
opts?):HistGradientBoostingRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.categorical_features? | number | Indicates the categorical features. |
opts.early_stopping? | boolean | "auto" | If ‘auto’, early stopping is enabled if the sample size is larger than 10000. If true, early stopping is enabled, otherwise early stopping is disabled. |
opts.interaction_cst? | "pairwise" | "no_interactions" | Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings “pairwise” and “no_interactions” are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, interaction_cst=\[{0, 1}\] is equivalent to interaction_cst=\[{0, 1}, {2, 3, 4}\], and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. |
opts.l2_regularization? | number | The L2 regularization parameter penalizing leaves with small hessians. Use 0 for no regularization (default). |
opts.learning_rate? | number | The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use 1 for no shrinkage. |
opts.loss? | "quantile" | "gamma" | "squared_error" | "absolute_error" | "poisson" | The loss function to use in the boosting process. Note that the “squared error”, “gamma” and “poisson” losses actually implement “half least squares loss”, “half gamma deviance” and “half poisson deviance” to simplify the computation of the gradient. Furthermore, “gamma” and “poisson” losses internally use a log-link, “gamma” requires y > 0 and “poisson” requires y >= 0. “quantile” uses the pinball loss. |
opts.max_bins? | number | The maximum number of bins to use for non-missing values. Before training, each feature of the input array X is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than max_bins bins. In addition to the max_bins bins, one more bin is always reserved for missing values. Must be no larger than 255. |
opts.max_depth? | number | The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn’t constrained by default. |
opts.max_features? | number | Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from interaction_cst are present, only allowed features are taken into account for the subsampling. |
opts.max_iter? | number | The maximum number of iterations of the boosting process, i.e. the maximum number of trees. |
opts.max_leaf_nodes? | number | The maximum number of leaves for each tree. Must be strictly greater than 1. If undefined, there is no maximum limit. |
opts.min_samples_leaf? | number | The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built. |
opts.monotonic_cst? | any | Monotonic constraint to enforce on each feature are specified using the following integer values: |
opts.n_iter_no_change? | number | Used to determine when to “early stop”. The fitting process is stopped when none of the last n_iter_no_change scores are better than the n_iter_no_change \- 1 -th-to-last one, up to some tolerance. Only used if early stopping is performed. |
opts.quantile? | number | If loss is “quantile”, this parameter specifies which quantile to be estimated and must be between 0 and 1. |
opts.random_state? | number | Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.scoring? | string | Scoring parameter to use for early stopping. It can be a single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions). If undefined, the estimator’s default scorer is used. If scoring='loss', early stopping is checked w.r.t the loss value. Only used if early stopping is performed. |
opts.tol? | number | The absolute tolerance to use when comparing scores during early stopping. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score. |
opts.validation_fraction? | number | Proportion (or absolute size) of training data to set aside as validation data for early stopping. If undefined, early stopping is done on the training data. Only used if early stopping is performed. |
opts.verbose? | number | The verbosity level. If not zero, print some information about the fitting process. |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only. See the Glossary. |
Returns HistGradientBoostingRegressor
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:29
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/ensemble/HistGradientBoostingRegressor.ts:27 |
_isInitialized | boolean | false | generated/ensemble/HistGradientBoostingRegressor.ts:26 |
_py | PythonBridge | undefined | generated/ensemble/HistGradientBoostingRegressor.ts:25 |
id | string | undefined | generated/ensemble/HistGradientBoostingRegressor.ts:22 |
opts | any | undefined | generated/ensemble/HistGradientBoostingRegressor.ts:23 |
Accessors
do_early_stopping_
Get Signature
get do_early_stopping_():
Promise<boolean>
Indicates whether early stopping is used during training.
Returns Promise<boolean>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:534
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/ensemble/HistGradientBoostingRegressor.ts:696
is_categorical_
Get Signature
get is_categorical_():
Promise<ArrayLike>
Boolean mask for the categorical features. undefined if there are no categorical features.
Returns Promise<ArrayLike>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:642
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:669
n_trees_per_iteration_
Get Signature
get n_trees_per_iteration_():
Promise<number>
The number of tree that are built at each iteration. For regressors, this is always 1.
Returns Promise<number>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:561
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:175
train_score_
Get Signature
get train_score_():
Promise<ArrayLike>
The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the scoring parameter. If scoring is not ‘loss’, scores are computed on a subset of at most 10 000 samples. Empty if no early stopping.
Returns Promise<ArrayLike>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:588
validation_score_
Get Signature
get validation_score_():
Promise<ArrayLike>
The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the scoring parameter. Empty if no early stopping or if validation_fraction is undefined.
Returns Promise<ArrayLike>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:615
Methods
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/ensemble/HistGradientBoostingRegressor.ts:231
fit()
fit(
opts):Promise<any>
Fit the gradient boosting model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | any | Weights of training data. |
opts.X? | ArrayLike[] | The input samples. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:248
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/ensemble/HistGradientBoostingRegressor.ts:296
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/ensemble/HistGradientBoostingRegressor.ts:188
predict()
predict(
opts):Promise<ArrayLike>
Predict values for X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. |
Returns Promise<ArrayLike>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:332
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/ensemble/HistGradientBoostingRegressor.ts:370
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.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:420
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/ensemble/HistGradientBoostingRegressor.ts:460
staged_predict()
staged_predict(
opts):Promise<any[]>
Predict regression target for each iteration.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns Promise<any[]>
Defined in generated/ensemble/HistGradientBoostingRegressor.ts:498