XGBoost

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from xgboost import XGBClassifier
import xgboost as xgb
import numpy as np
from sklearn.model_selection import GridSearchCV

def model_xgb(X_train,y_train):

    #xgb_train = xgb.DMatrix(X_train,label=y_train)

    model = XGBClassifier(
        learning_rate=0.1,
        n_estimators=500,
        max_depth=5,
        min_child_weight=1,
        subsample=0.8,
        colsample_bytree=0.8,
        gamma=0,
        reg_alpha=0,
        reg_lambda=1,
        nthread=4
    )
    param_dist = {
        #"max_depth": [10,30,50],
        #"min_child_weight" : [1],
        "n_estimators": np.arange(400,800,100),
        #"learning_rate": [0.16],
    }
    grid_search = GridSearchCV(
        model,
        param_grid=param_dist,
        cv = 3,
        verbose=10,
        n_jobs=-1
    )

    #grid_search.fit(X_train, y_train)
    #print(grid_search.best_estimator_)
    #print('参数的最佳取值：{0}'.format(grid_search.best_params_))
    #print('最佳模型得分:{0}'.format(grid_search.best_score_))


    xgb_model_best = model.fit(X_train,y_train)
    #y_prob = xgb_model_best.predict_prob(X_test)[:,1]
    #y_pred = np.where(y_prob>0.5,1,0)

    return xgb_model_best