Multiclass classification with xgboost classifier?

Question

I am trying out multi-class classification with xgboost and I\'ve built it using this code,

clf = xgb.XGBClassifier(max_depth=7, n_estimators=1000)

clf.fit(byte         


        

Answer 1

In fact, even if the default obj parameter of XGBClassifier is binary:logistic, it will internally judge the number of class of label y. When the class number is greater than 2, it will modify the obj parameter to multi:softmax.

https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/sklearn.py

class XGBClassifier(XGBModel, XGBClassifierBase):
    # pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
    def __init__(self, objective="binary:logistic", **kwargs):
        super().__init__(objective=objective, **kwargs)

    def fit(self, X, y, sample_weight=None, base_margin=None,
            eval_set=None, eval_metric=None,
            early_stopping_rounds=None, verbose=True, xgb_model=None,
            sample_weight_eval_set=None, callbacks=None):
        # pylint: disable = attribute-defined-outside-init,arguments-differ

        evals_result = {}
        self.classes_ = np.unique(y)
        self.n_classes_ = len(self.classes_)

        xgb_options = self.get_xgb_params()

        if callable(self.objective):
            obj = _objective_decorator(self.objective)
            # Use default value. Is it really not used ?
            xgb_options["objective"] = "binary:logistic"
        else:
            obj = None

        if self.n_classes_ > 2:
            # Switch to using a multiclass objective in the underlying
            # XGB instance
            xgb_options['objective'] = 'multi:softprob'
            xgb_options['num_class'] = self.n_classes_