xgboost.sklearn.XGBClassifier = class XGBClassifier(XGBModel, sklearn.base.ClassifierMixin)
| Implementation of the scikit-learn API for XGBoost classification.
|
| Parameters
| ----------
| max_depth : int
| Maximum tree depth for base learners.
| learning_rate : float
| Boosting learning rate (xgb's "eta")
| n_estimators : int
| Number of boosted trees to fit.
| silent : boolean
| Whether to print messages while running boosting.
| objective : string or callable
| Specify the learning task and the corresponding learning objective or
| a custom objective function to be used (see note below).
| nthread : int
| Number of parallel threads used to run xgboost.
| gamma : float
| Minimum loss reduction required to make a further partition on a leaf node of the tree.
| min_child_weight : int
| Minimum sum of instance weight(hessian) needed in a child.
| max_delta_step : int
| Maximum delta step we allow each tree's weight estimation to be.
| subsample : float
| Subsample ratio of the training instance.
| colsample_bytree : float
| Subsample ratio of columns when constructing each tree.
| colsample_bylevel : float
| Subsample ratio of columns for each split, in each level.
| reg_alpha : float (xgb's alpha)
| L1 regularization term on weights
| reg_lambda : float (xgb's lambda)
| L2 regularization term on weights
| scale_pos_weight : float
| Balancing of positive and negative weights.
|
| base_score:
| The initial prediction score of all instances, global bias.
| seed : int
| Random number seed.
| missing : float, optional
| Value in the data which needs to be present as a missing value. If
| None, defaults to np.nan.
|
| Note
| ----
| A custom objective function can be provided for the ``objective``
| parameter. In this case, it should have the signature
| ``objective(y_true, y_pred) -> grad, hess``:
|
| y_true: array_like of shape [n_samples]
| The target values
| y_pred: array_like of shape [n_samples]
| The predicted values
|
| grad: array_like of shape [n_samples]
| The value of the gradient for each sample point.
| hess: array_like of shape [n_samples]
| The value of the second derivative for each sample point
|
| Method resolution order:
| XGBClassifier
| XGBModel
| sklearn.base.BaseEstimator
| sklearn.base.ClassifierMixin
| builtins.object
|
| Methods defined here:
|
| __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='binary:logistic', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)
| Initialize self. See help(type(self)) for accurate signature.
|
| evals_result(self)
| Return the evaluation results.
|
| If eval_set is passed to the `fit` function, you can call evals_result() to
| get evaluation results for all passed eval_sets. When eval_metric is also
| passed to the `fit` function, the evals_result will contain the eval_metrics
| passed to the `fit` function
|
| Returns
| -------
| evals_result : dictionary
|
| Example
| -------
| param_dist = {'objective':'binary:logistic', 'n_estimators':2}
|
| clf = xgb.XGBClassifier(**param_dist)
|
| clf.fit(X_train, y_train,
| eval_set=[(X_train, y_train), (X_test, y_test)],
| eval_metric='logloss',
| verbose=True)
|
| evals_result = clf.evals_result()
|
| The variable evals_result will contain:
| {'validation_0': {'logloss': ['0.604835', '0.531479']},
| 'validation_1': {'logloss': ['0.41965', '0.17686']}}
|
| fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True)
| Fit gradient boosting classifier
|
| Parameters
| ----------
| X : array_like
| Feature matrix
| y : array_like
| Labels
| sample_weight : array_like
| Weight for each instance
| eval_set : list, optional
| A list of (X, y) pairs to use as a validation set for
| early-stopping
| eval_metric : str, callable, optional
| If a str, should be a built-in evaluation metric to use. See
| doc/parameter.md. If callable, a custom evaluation metric. The call
| signature is func(y_predicted, y_true) where y_true will be a
| DMatrix object such that you may need to call the get_label
| method. It must return a str, value pair where the str is a name
| for the evaluation and value is the value of the evaluation
| function. This objective is always minimized.
| early_stopping_rounds : int, optional
| Activates early stopping. Validation error needs to decrease at
| least every <early_stopping_rounds> round(s) to continue training.
| Requires at least one item in evals. If there's more than one,
| will use the last. Returns the model from the last iteration
| (not the best one). If early stopping occurs, the model will
| have three additional fields: bst.best_score, bst.best_iteration
| and bst.best_ntree_limit.
| (Use bst.best_ntree_limit to get the correct value if num_parallel_tree
| and/or num_class appears in the parameters)
| verbose : bool
| If `verbose` and an evaluation set is used, writes the evaluation
| metric measured on the validation set to stderr.
|
| predict(self, data, output_margin=False, ntree_limit=0)
|
| predict_proba(self, data, output_margin=False, ntree_limit=0)
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| feature_importances_
| Returns
| -------
| feature_importances_ : array of shape = [n_features]
|
| ----------------------------------------------------------------------
| Methods inherited from XGBModel:
|
| __setstate__(self, state)
|
| apply(self, X, ntree_limit=0)
| Return the predicted leaf every tree for each sample.
|
| Parameters
| ----------
| X : array_like, shape=[n_samples, n_features]
| Input features matrix.
|
| ntree_limit : int
| Limit number of trees in the prediction; defaults to 0 (use all trees).
|
| Returns
| -------
| X_leaves : array_like, shape=[n_samples, n_trees]
| For each datapoint x in X and for each tree, return the index of the
| leaf x ends up in. Leaves are numbered within
| ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.
|
| booster(self)
| Get the underlying xgboost Booster of this model.
|
| This will raise an exception when fit was not called
|
| Returns
| -------
| booster : a xgboost booster of underlying model
|
| get_params(self, deep=False)
| Get parameter.s
|
| get_xgb_params(self)
| Get xgboost type parameters.
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.BaseEstimator:
|
| __getstate__(self)
|
| __repr__(self)
| Return repr(self).
|
| set_params(self, **params)
| Set the parameters of this estimator.
|
| The method works on simple estimators as well as on nested objects
| (such as pipelines). The latter have parameters of the form
| ``<component>__<parameter>`` so that it's possible to update each
| component of a nested object.
|
| Returns
| -------
| self
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sklearn.base.BaseEstimator:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.ClassifierMixin:
|
| score(self, X, y, sample_weight=None)
| Returns the mean accuracy on the given test data and labels.
|
| In multi-label classification, this is the subset accuracy
| which is a harsh metric since you require for each sample that
| each label set be correctly predicted.
|
| Parameters
| ----------
| X : array-like, shape = (n_samples, n_features)
| Test samples.
|
| y : array-like, shape = (n_samples) or (n_samples, n_outputs)
| True labels for X.
|
| sample_weight : array-like, shape = [n_samples], optional
| Sample weights.
|
| Returns
| -------
| score : float
| Mean accuracy of self.predict(X) wrt. y.
