ML之xgboost：利用xgboost算法(sklearn+GridSearchCV)训练mushroom蘑菇数据集(22+1,6513+1611)来预测蘑菇是否毒性(二分类预测)

目录

输出结果

设计思路

核心代码

更多输出

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输出结果

正在更新……

设计思路

正在更新……

核心代码

from sklearn.grid_search import GridSearchCVparam_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 pltx = 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()

更多输出

GridSearchCV time： 79.7655139499154
clf.grid_scores_： [mean: 0.90542, std: 0.08996, params: {'n_estimators': 1}, mean: 0.94749, std: 0.07458, params: {'n_estimators': 2}, mean: 0.90542, std: 0.08996, params: {'n_estimators': 3}, mean: 0.94749, std: 0.07458, params: {'n_estimators': 4}, mean: 0.90573, std: 0.09028, params: {'n_estimators': 5}, mean: 0.94718, std: 0.07436, params: {'n_estimators': 6}, mean: 0.90542, std: 0.08996, params: {'n_estimators': 7}, mean: 0.94242, std: 0.07331, params: {'n_estimators': 8}, mean: 0.94473, std: 0.07739, params: {'n_estimators': 9}, mean: 0.97482, std: 0.02235, params: {'n_estimators': 10}, mean: 0.94887, std: 0.07621, params: {'n_estimators': 11}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 12}, mean: 0.97298, std: 0.03186, params: {'n_estimators': 13}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 14}, mean: 0.97298, std: 0.03186, params: {'n_estimators': 15}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 16}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 17}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 18}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 19}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 20}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 21}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 22}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 23}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 24}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 25}, mean: 0.97804, std: 0.02365, params: {'n_estimators': 26}, mean: 0.97774, std: 0.02337, params: {'n_estimators': 27}, mean: 0.97835, std: 0.02383, params: {'n_estimators': 28}, mean: 0.98296, std: 0.01963, params: {'n_estimators': 29}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 30}, mean: 0.98342, std: 0.01988, params: {'n_estimators': 31}, mean: 0.98372, std: 0.02008, params: {'n_estimators': 32}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 33}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 34}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 35}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 36}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 37}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 38}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 39}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 40}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 41}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 42}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 43}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 44}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 45}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 46}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 47}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 48}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 49}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 50}]
clf.best_params_： {'n_estimators': 30}
clf.best_score_： 0.9841854752034392

[mean: 0.90542, std: 0.08996, params: {'n_estimators': 1}, mean: 0.94749, std: 0.07458, params: {'n_estimators': 2}, mean: 0.90542, std: 0.08996, params: {'n_estimators': 3}, mean: 0.94749, std: 0.07458, params: {'n_estimators': 4}, mean: 0.90573, std: 0.09028, params: {'n_estimators': 5}, mean: 0.94718, std: 0.07436, params: {'n_estimators': 6}, mean: 0.90542, std: 0.08996, params: {'n_estimators': 7}, mean: 0.94242, std: 0.07331, params: {'n_estimators': 8}, mean: 0.94473, std: 0.07739, params: {'n_estimators': 9}, mean: 0.97482, std: 0.02235, params: {'n_estimators': 10}, mean: 0.94887, std: 0.07621, params: {'n_estimators': 11}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 12}, mean: 0.97298, std: 0.03186, params: {'n_estimators': 13}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 14}, mean: 0.97298, std: 0.03186, params: {'n_estimators': 15}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 16}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 17}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 18}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 19}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 20}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 21}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 22}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 23}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 24}, mean: 0.97850, std: 0.02387, params: {'n_estimators': 25}, mean: 0.97804, std: 0.02365, params: {'n_estimators': 26}, mean: 0.97774, std: 0.02337, params: {'n_estimators': 27}, mean: 0.97835, std: 0.02383, params: {'n_estimators': 28}, mean: 0.98296, std: 0.01963, params: {'n_estimators': 29}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 30}, mean: 0.98342, std: 0.01988, params: {'n_estimators': 31}, mean: 0.98372, std: 0.02008, params: {'n_estimators': 32}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 33}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 34}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 35}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 36}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 37}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 38}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 39}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 40}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 41}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 42}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 43}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 44}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 45}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 46}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 47}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 48}, mean: 0.98419, std: 0.02040, params: {'n_estimators': 49}]grid_scores_ = [mean: 0.90542, std: 0.08996, mean: 0.94749, std: 0.07458, mean: 0.90542, std: 0.08996, mean: 0.94749, std: 0.07458, mean: 0.90573, std: 0.09028, mean: 0.94718, std: 0.07436,mean: 0.90542, std: 0.08996, mean: 0.94242, std: 0.07331, mean: 0.94473, std: 0.07739,  mean: 0.97482, std: 0.02235,mean: 0.94887, std: 0.07621, mean: 0.97850, std: 0.02387, mean: 0.97298, std: 0.03186,  mean: 0.97850, std: 0.02387, mean: 0.97298, std: 0.03186,  mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387,  mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387,mean: 0.97850, std: 0.02387, mean: 0.97850, std: 0.02387,mean: 0.97804, std: 0.02365,  mean: 0.97774, std: 0.02337, mean: 0.97835, std: 0.02383, mean: 0.98296, std: 0.01963, mean: 0.98419, std: 0.02040,  mean: 0.98342, std: 0.01988, mean: 0.98372, std: 0.02008, mean: 0.98419, std: 0.02040,  mean: 0.98419, std: 0.02040,  mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040, mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040, mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040,mean: 0.98419, std: 0.02040  ]

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