3 examples of 'xgbclassifier python' in Python

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def run_xgb(train_X, test_X, train_y, test_y, depth=6, a=0.0, l=1.5, seed=0):
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	param = {'max_depth':depth, 'num_round':20, 'eta':0.3, 'silent':1,
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				'objective':'binary:logistic', 'eval_metric':['auc', 'error'],
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				'alpha': a, 'lambda':l }
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	if seed != 0:  # specific random seed entered
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		param['seed'] = seed
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		param['colsample_bytree'] = 0.5
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		param['colsample_bylevel'] = 0.5
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	train_xgb = xgb.DMatrix(train_X, label=train_y)
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	test_xgb = xgb.DMatrix(test_X, label=test_y)
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	bst = xgb.train(param, train_xgb)
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	ypred = bst.predict(test_xgb)
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	metrics = gen_eval_metrics(test_y, ypred)
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	accuracy = metrics[0]
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	#cor = sum([int(ypred[i] + 0.5) == test_y[i] for i in range(len(ypred))])
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	#accuracy = cor / len(test_y)
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	print('Fold accuracy: ' + str(accuracy))
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	return metrics

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def test_xgboost_multiclass():
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    try:
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        import xgboost
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    except Exception as e:
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        print("Skipping test_xgboost_multiclass!")
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        return
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    import shap
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    # train XGBoost model
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    X, Y = shap.datasets.iris()
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    model = xgboost.XGBClassifier(objective="binary:logistic", max_depth=4)
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    model.fit(X, Y)
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    # explain the model's predictions using SHAP values (use pred_contrib in LightGBM)
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    shap_values = shap.TreeExplainer(model).shap_values(X)
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    # ensure plot works for first class
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    shap.dependence_plot(0, shap_values[0], X, show=False)

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def train_model(self, train_x, train_y):
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    """
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    use Feature vector
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    :param train_x:
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    :param train_y:
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    :return:
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    """
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    self.clf = xgb.XGBClassifier()
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    self.clf.fit(train_x, train_y, eval_metric=self.eval_metric,
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                 eval_set=[(train_x, train_y)])
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    self.init = True
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    evals_result = self.clf.evals_result()
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    print('evals_result:', evals_result)
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    with open(self.xgb_model_name, 'wb')as f:
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        pickle.dump(self.clf, f, True)