ML之xgboost：利用xgboost算法(sklearn+3Split+调参曲线)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
输出结果
设计思路
核心代码
eval_set = [(X_train_part, y_train_part), (X_validate, y_validate)] bst.fit(X_train_part, y_train_part, eval_metric=["error", "logloss"], eval_set=eval_set, verbose=True)preds = bst.predict(X_test)predictions = [round(value) for value in preds]test_accuracy = accuracy_score(y_test, predictions)print("【max_depth=2,lr=0.1】Test Accuracy: %.2f%%" % (test_accuracy * 100.0))results = bst.evals_result()
更多输出
X_train： (6513, 126)X_test： (1611, 126)After split(33%)，X_train_part： (4363, 126)After split(33%)，X_validate： (2150, 126)[0]validation_0-error:0.045611validation_0-logloss:0.614637validation_1-error:0.048372validation_1-logloss:0.615401[1]validation_0-error:0.041256validation_0-logloss:0.549907validation_1-error:0.042326validation_1-logloss:0.550696[2]validation_0-error:0.045611validation_0-logloss:0.49543validation_1-error:0.048372validation_1-logloss:0.496777[3]validation_0-error:0.041256validation_0-logloss:0.449089validation_1-error:0.042326validation_1-logloss:0.450412[4]validation_0-error:0.041256validation_0-logloss:0.409231validation_1-error:0.042326validation_1-logloss:0.410717[5]validation_0-error:0.041256validation_0-logloss:0.373748validation_1-error:0.042326validation_1-logloss:0.375653[6]validation_0-error:0.023378validation_0-logloss:0.343051validation_1-error:0.023256validation_1-logloss:0.344738[7]validation_0-error:0.041256validation_0-logloss:0.315369validation_1-error:0.042326validation_1-logloss:0.317409[8]validation_0-error:0.041256validation_0-logloss:0.290912validation_1-error:0.042326validation_1-logloss:0.292587[9]validation_0-error:0.023378validation_0-logloss:0.269356validation_1-error:0.023256validation_1-logloss:0.271103[10]validation_0-error:0.00573validation_0-logloss:0.249593validation_1-error:0.006512validation_1-logloss:0.251354[11]validation_0-error:0.01719validation_0-logloss:0.228658validation_1-error:0.017674validation_1-logloss:0.230144[12]validation_0-error:0.01719validation_0-logloss:0.210442validation_1-error:0.017674validation_1-logloss:0.21167[13]validation_0-error:0.01719validation_0-logloss:0.194562validation_1-error:0.017674validation_1-logloss:0.19555[14]validation_0-error:0.01719validation_0-logloss:0.1807validation_1-error:0.017674validation_1-logloss:0.181463[15]validation_0-error:0.01719validation_0-logloss:0.168585validation_1-error:0.017674validation_1-logloss:0.169138[16]validation_0-error:0.01719validation_0-logloss:0.157988validation_1-error:0.017674validation_1-logloss:0.158345[17]validation_0-error:0.01719validation_0-logloss:0.149407validation_1-error:0.017674validation_1-logloss:0.149731[18]validation_0-error:0.0259validation_0-logloss:0.140835validation_1-error:0.024651validation_1-logloss:0.140979[19]validation_0-error:0.02validation_0-logloss:0.133937validation_1-error:0.020465validation_1-logloss:0.13405[20]validation_0-error:0.02validation_0-logloss:0.126967validation_1-error:0.020465validation_1-logloss:0.126914[21]validation_0-error:0.02validation_0-logloss:0.121386validation_1-error:0.020465validation_1-logloss:0.121303[22]validation_0-error:0.02validation_0-logloss:0.115692validation_1-error:0.020465validation_1-logloss:0.115456[23]validation_0-error:0.02validation_0-logloss:0.111147validation_1-error:0.020465validation_1-logloss:0.110881[24]validation_0-error:0.02validation_0-logloss:0.106477validation_1-error:0.020465validation_1-logloss:0.10607[25]validation_0-error:0.02validation_0-logloss:0.102434validation_1-error:0.020465validation_1-logloss:0.102319[26]validation_0-error:0.02validation_0-logloss:0.098434validation_1-error:0.020465validation_1-logloss:0.09819[27]validation_0-error:0.02validation_0-logloss:0.094875validation_1-error:0.020465validation_1-logloss:0.094824[28]validation_0-error:0.02validation_0-logloss:0.091579validation_1-error:0.020465validation_1-logloss:0.091784[29]validation_0-error:0.013294validation_0-logloss:0.086202validation_1-error:0.013488validation_1-logloss:0.086807[30]validation_0-error:0.02validation_0-logloss:0.083247validation_1-error:0.020465validation_1-logloss:0.083741[31]validation_0-error:0.02validation_0-logloss:0.080496validation_1-error:0.020465validation_1-logloss:0.080924[32]validation_0-error:0.02validation_0-logloss:0.077298validation_1-error:0.020465validation_1-logloss:0.077394[33]validation_0-error:0.015815validation_0-logloss:0.074507validation_1-error:0.016279validation_1-logloss:0.074765[34]validation_0-error:0.02validation_0-logloss:0.071848validation_1-error:0.020465validation_1-logloss:0.071811[35]validation_0-error:0.010543validation_0-logloss:0.069488validation_1-error:0.009302validation_1-logloss:0.069385[36]validation_0-error:0.001834validation_0-logloss:0.067147validation_1-error:0.002326validation_1-logloss:0.067341[37]validation_0-error:0.001834validation_0-logloss:0.06504validation_1-error:0.002326validation_1-logloss:0.065406[38]validation_0-error:0.001834validation_0-logloss:0.062898validation_1-error:0.002326validation_1-logloss:0.063381[39]validation_0-error:0.001834validation_0-logloss:0.060837validation_1-error:0.002326validation_1-logloss:0.061088[40]validation_0-error:0.001834validation_0-logloss:0.058894validation_1-error:0.002326validation_1-logloss:0.059039[41]validation_0-error:0.001834validation_0-logloss:0.057112validation_1-error:0.002326validation_1-logloss:0.057326[42]validation_0-error:0.001834validation_0-logloss:0.055391validation_1-error:0.002326validation_1-logloss:0.05543[43]validation_0-error:0.001834validation_0-logloss:0.053745validation_1-error:0.002326validat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Accuracy: 99.81%{'validation_0': {'error': [0.045611, 0.041256, 0.045611, 0.041256, 0.041256, 0.041256, 0.023378, 0.041256, 0.041256, 0.023378, 0.00573, 0.01719, 0.01719, 0.01719, 0.01719, 0.01719, 0.01719, 0.01719, 0.0259, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.013294, 0.02, 0.02, 0.02, 0.015815, 0.02, 0.010543, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834, 0.001834], 'logloss': [0.614637, 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ML之xgboost：利用xgboost算法(sklearn+3Split+调参曲线)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)
