输出结果
核心代码
from sklearn.grid_search import GridSearchCV
param_test = { 'n_estimators': range(1, 51, 1)}
clf = GridSearchCV(estimator = bst, param_grid = param_test, cv=5)
clf.fit(X_train, y_train)
clf.grid_scores_, clf.best_params_, clf.best_score_
grid_scores_mean= [0.90542, 0.94749, 0.90542, 0.94749, 0.90573, 0.94718,
0.90542, 0.94242, 0.94473, 0.97482, 0.94887, 0.97850,
0.97298, 0.97850, 0.97298, 0.97850, 0.97850, 0.97850,
0.97850, 0.97850, 0.97850, 0.97850, 0.97850, 0.97850,
0.97850, 0.97804, 0.97774, 0.97835, 0.98296, 0.98419,
0.98342, 0.98372, 0.98419, 0.98419, 0.98419, 0.98419,
0.98419, 0.98419, 0.98419, 0.98419, 0.98419, 0.98419,
0.98419, 0.98419, 0.98419, 0.98419, 0.98419, 0.98419,
0.98419 ]
grid_scores_std = [0.08996, 0.07458, 0.08996, 0.07458, 0.09028, 0.07436,
0.08996, 0.07331, 0.07739, 0.02235, 0.07621, 0.02387,
0.03186, 0.02387, 0.03186, 0.02387, 0.02387, 0.02387,
0.02387, 0.02387, 0.02387, 0.02387, 0.02387, 0.02387,
0.02387, 0.02365, 0.02337, 0.02383, 0.01963, 0.02040,
0.01988, 0.02008, 0.02040, 0.02040, 0.02040, 0.02040,
0.02040, 0.02040, 0.02040, 0.02040, 0.02040, 0.02040,
0.02040, 0.02040, 0.02040, 0.02040, 0.02040, 0.02040,
0.02040 ]
#7-CrVa交叉验证曲线可视化
import matplotlib.pyplot as plt
x = range(0,len(grid_scores_mean))
y1 = grid_scores_mean
y2 = grid_scores_std
Xlabel = 'n_estimators'
Ylabel = 'value'
title = 'mushroom datase: xgboost(sklearn+GridSearchCV) model'
plt.plot(x,y1,'r',label='Mean') #绘制mean曲线
plt.plot(x,y2,'g',label='Std') #绘制std曲线
plt.rcParams['font.sans-serif']=['Times New Roman'] #手动添加中文字体，或者['font.sans-serif'] = ['FangSong'] SimHei
#myfont = matplotlib.font_manager.FontProperties(fname='C:/Windows/Fonts/msyh.ttf') #也可以指定win系统字体路径
plt.rcParams['axes.unicode_minus'] = False #对坐标轴的负号进行正常显示
plt.xlabel(Xlabel)
plt.ylabel(Ylabel)
plt.title(title)
plt.legend(loc=1)
plt.show()