hyperimpute.plugins.prediction.classifiers.plugin_xgboost module
- class XGBoostPlugin(n_estimators: int = 100, reg_lambda: Optional[float] = None, reg_alpha: Optional[float] = None, colsample_bytree: Optional[float] = None, colsample_bynode: Optional[float] = None, colsample_bylevel: Optional[float] = None, max_depth: Optional[int] = 3, subsample: Optional[float] = None, lr: Optional[float] = None, min_child_weight: Optional[int] = None, max_bin: int = 256, booster: int = 0, grow_policy: int = 0, nthread: int = 1, random_state: int = 0, eta: float = 0.3, hyperparam_search_iterations: Optional[int] = None, **kwargs: Any)
Bases:
ClassifierPlugin
Classification plugin based on the XGBoost classifier.
- Method:
Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. The XGBoost algorithm has a robust handling of a variety of data types, relationships, distributions, and the variety of hyperparameters that you can fine-tune.
- Parameters:
n_estimators – int The maximum number of estimators at which boosting is terminated.
max_depth – int Maximum depth of a tree.
reg_lambda – float L2 regularization term on weights (xgb’s lambda).
reg_alpha – float L1 regularization term on weights (xgb’s alpha).
colsample_bytree – float Subsample ratio of columns when constructing each tree.
colsample_bynode – float Subsample ratio of columns for each split.
colsample_bylevel – float Subsample ratio of columns for each level.
subsample – float Subsample ratio of the training instance.
lr – float Boosting learning rate
booster – str Specify which booster to use: gbtree, gblinear or dart.
min_child_weight – int Minimum sum of instance weight(hessian) needed in a child.
max_bin – int Number of bins for histogram construction.
random_state – float Random number seed.
Example
>>> from hyperimpute.plugins.prediction import Predictions >>> plugin = Predictions(category="classifiers").get("xgboost") >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> plugin.fit_predict(X, y)
- _abc_impl = <_abc_data object>
- _fit(X: DataFrame, *args: Any, **kwargs: Any) XGBoostPlugin
- _predict(X: DataFrame, *args: Any, **kwargs: Any) DataFrame
- _predict_proba(X: DataFrame, *args: Any, **kwargs: Any) DataFrame
- booster = ['gbtree', 'gblinear', 'dart']
- grow_policy = ['depthwise', 'lossguide']
- module_relative_path: Optional[Path]
- static name() str
- plugin
alias of
XGBoostPlugin