ML之xgboost：利用xgboost算法(sklearn+GridSearchCV)訓(xùn)練mushroom蘑菇數(shù)據(jù)集(22+1,6513+1611)來(lái)預(yù)測(cè)蘑菇是否毒性(二分類預(yù)測(cè))

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

更多輸出

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輸出結(jié)果

正在更新……

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設(shè)計(jì)思路

正在更新……

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核心代碼

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交叉驗(yàn)證曲線可視化 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') #繪制mean曲線 plt.plot(x,y2,'g',label='Std') #繪制std曲線plt.rcParams['font.sans-serif']=['Times New Roman'] #手動(dòng)添加中文字體，或者['font.sans-serif'] = ['FangSong'] SimHei #myfont = matplotlib.font_manager.FontProperties(fname='C:/Windows/Fonts/msyh.ttf') #也可以指定win系統(tǒng)字體路徑 plt.rcParams['axes.unicode_minus'] = False #對(duì)坐標(biāo)軸的負(fù)號(hào)進(jìn)行正常顯示plt.xlabel(Xlabel) plt.ylabel(Ylabel) plt.title(title)plt.legend(loc=1) plt.show()

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更多輸出

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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總結(jié)

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