Statements (32)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
regression model |
| gptkbp:availableOn |
gptkb:scikit-learn
|
| gptkbp:basedOn |
gradient boosting
|
| gptkbp:baseEstimator |
gptkb:tree
|
| gptkbp:canBe |
categorical features (with preprocessing)
numerical features |
| gptkbp:developedBy |
gptkb:scikit-learn
|
| gptkbp:documentation |
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
|
| gptkbp:ensembleMethod |
yes
|
| gptkbp:featureImportancesAttribute |
feature_importances_
|
| gptkbp:fitMethod |
fit(X, y)
|
| https://www.w3.org/2000/01/rdf-schema#label |
GradientBoostingRegressor
|
| gptkbp:hyperparameter |
loss
learning_rate max_depth max_features min_samples_leaf min_samples_split n_estimators subsample |
| gptkbp:implementedIn |
gptkb:Python
|
| gptkbp:introducedIn |
scikit-learn 0.9
|
| gptkbp:output |
continuous values
|
| gptkbp:predictMethod |
predict(X)
|
| gptkbp:randomStateParameter |
random_state
|
| gptkbp:supports |
early stopping
feature importance custom loss functions |
| gptkbp:usedFor |
regression
|
| gptkbp:bfsParent |
gptkb:scikit-learn_GradientBoosting
|
| gptkbp:bfsLayer |
6
|