ML之xgboost：利用xgboost算法(sklearn+3Split+調參曲線)訓練mushroom蘑菇數據集(22+1,6513+1611)來預測蘑菇是否毒性(二分類預測)

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目錄

輸出結果

設計思路

核心代碼

更多輸出

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輸出結果

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設計思路

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核心代碼

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()

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更多輸出

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:0.045611 validation_0-logloss:0.614637 validation_1-error:0.048372 validation_1-logloss:0.615401 [1] validation_0-error:0.041256 validation_0-logloss:0.549907 validation_1-error:0.042326 validation_1-logloss:0.550696 [2] validation_0-error:0.045611 validation_0-logloss:0.49543 validation_1-error:0.048372 validation_1-logloss:0.496777 [3] validation_0-error:0.041256 validation_0-logloss:0.449089 validation_1-error:0.042326 validation_1-logloss:0.450412 [4] validation_0-error:0.041256 validation_0-logloss:0.409231 validation_1-error:0.042326 validation_1-logloss:0.410717 [5] validation_0-error:0.041256 validation_0-logloss:0.373748 validation_1-error:0.042326 validation_1-logloss:0.375653 [6] validation_0-error:0.023378 validation_0-logloss:0.343051 validation_1-error:0.023256 validation_1-logloss:0.344738 [7] validation_0-error:0.041256 validation_0-logloss:0.315369 validation_1-error:0.042326 validation_1-logloss:0.317409 [8] validation_0-error:0.041256 validation_0-logloss:0.290912 validation_1-error:0.042326 validation_1-logloss:0.292587 [9] validation_0-error:0.023378 validation_0-logloss:0.269356 validation_1-error:0.023256 validation_1-logloss:0.271103 [10] validation_0-error:0.00573 validation_0-logloss:0.249593 validation_1-error:0.006512 validation_1-logloss:0.251354 [11] validation_0-error:0.01719 validation_0-logloss:0.228658 validation_1-error:0.017674 validation_1-logloss:0.230144 [12] validation_0-error:0.01719 validation_0-logloss:0.210442 validation_1-error:0.017674 validation_1-logloss:0.21167 [13] validation_0-error:0.01719 validation_0-logloss:0.194562 validation_1-error:0.017674 validation_1-logloss:0.19555 [14] validation_0-error:0.01719 validation_0-logloss:0.1807 validation_1-error:0.017674 validation_1-logloss:0.181463 [15] validation_0-error:0.01719 validation_0-logloss:0.168585 validation_1-error:0.017674 validation_1-logloss:0.169138 [16] validation_0-error:0.01719 validation_0-logloss:0.157988 validation_1-error:0.017674 validation_1-logloss:0.158345 [17] validation_0-error:0.01719 validation_0-logloss:0.149407 validation_1-error:0.017674 validation_1-logloss:0.149731 [18] validation_0-error:0.0259 validation_0-logloss:0.140835 validation_1-error:0.024651 validation_1-logloss:0.140979 [19] validation_0-error:0.022003 validation_0-logloss:0.133937 validation_1-error:0.020465 validation_1-logloss:0.13405 [20] validation_0-error:0.022003 validation_0-logloss:0.126967 validation_1-error:0.020465 validation_1-logloss:0.126914 [21] validation_0-error:0.022003 validation_0-logloss:0.121386 validation_1-error:0.020465 validation_1-logloss:0.121303 [22] validation_0-error:0.022003 validation_0-logloss:0.115692 validation_1-error:0.020465 validation_1-logloss:0.115456 [23] validation_0-error:0.022003 validation_0-logloss:0.111147 validation_1-error:0.020465 validation_1-logloss:0.110881 [24] validation_0-error:0.022003 validation_0-logloss:0.106477 validation_1-error:0.020465 validation_1-logloss:0.10607 [25] validation_0-error:0.022003 validation_0-logloss:0.102434 validation_1-error:0.020465 validation_1-logloss:0.102319 [26] validation_0-error:0.022003 validation_0-logloss:0.098434 validation_1-error:0.020465 validation_1-logloss:0.09819 [27] validation_0-error:0.022003 validation_0-logloss:0.094875 validation_1-error:0.020465 validation_1-logloss:0.094824 [28] validation_0-error:0.022003 validation_0-logloss:0.091579 validation_1-error:0.020465 validation_1-logloss:0.091784 [29] validation_0-error:0.013294 validation_0-logloss:0.086202 validation_1-error:0.013488 validation_1-logloss:0.086807 [30] validation_0-error:0.022003 validation_0-logloss:0.083247 validation_1-error:0.020465 validation_1-logloss:0.083741 [31] validation_0-error:0.022003 validation_0-logloss:0.080496 validation_1-error:0.020465 validation_1-logloss:0.080924 [32] validation_0-error:0.022003 validation_0-logloss:0.077298 validation_1-error:0.020465 validation_1-logloss:0.077394 [33] validation_0-error:0.015815 validation_0-logloss:0.074507 validation_1-error:0.016279 validation_1-logloss:0.074765 [34] validation_0-error:0.022003 validation_0-logloss:0.071848 validation_1-error:0.020465 validation_1-logloss:0.071811 [35] validation_0-error:0.010543 validation_0-logloss:0.069488 validation_1-error:0.009302 validation_1-logloss:0.069385 [36] validation_0-error:0.001834 validation_0-logloss:0.067147 validation_1-error:0.002326 validation_1-logloss:0.067341 [37] validation_0-error:0.001834 validation_0-logloss:0.06504 validation_1-error:0.002326 validation_1-logloss:0.065406 [38] validation_0-error:0.001834 validation_0-logloss:0.062898 validation_1-error:0.002326 validation_1-logloss:0.063381 [39] validation_0-error:0.001834 validation_0-logloss:0.060837 validation_1-error:0.002326 validation_1-logloss:0.061088 [40] validation_0-error:0.001834 validation_0-logloss:0.058894 validation_1-error:0.002326 validation_1-logloss:0.059039 [41] validation_0-error:0.001834 validation_0-logloss:0.057112 validation_1-error:0.002326 validation_1-logloss:0.057326 [42] validation_0-error:0.001834 validation_0-logloss:0.055391 validation_1-error:0.002326 validation_1-logloss:0.05543 [43] validation_0-error:0.001834 validation_0-logloss:0.053745 validation_1-error:0.002326 validation_1-logloss:0.053871 [44] validation_0-error:0.001834 validation_0-logloss:0.052198 validation_1-error:0.002326 validation_1-logloss:0.052235 [45] validation_0-error:0.001834 validation_0-logloss:0.050776 validation_1-error:0.002326 validation_1-logloss:0.051033 [46] validation_0-error:0.001834 validation_0-logloss:0.049351 validation_1-error:0.002326 validation_1-logloss:0.04973 [47] validation_0-error:0.001834 validation_0-logloss:0.047848 validation_1-error:0.002326 validation_1-logloss:0.048287 [48] validation_0-error:0.001834 validation_0-logloss:0.046406 validation_1-error:0.002326 validation_1-logloss:0.046702 [49] validation_0-error:0.001834 validation_0-logloss:0.045141 validation_1-error:0.002326 validation_1-logloss:0.045492 [50] validation_0-error:0.001834 validation_0-logloss:0.043917 validation_1-error:0.002326 validation_1-logloss:0.044133 [51] validation_0-error:0.001834 validation_0-logloss:0.042729 validation_1-error:0.002326 validation_1-logloss:0.042999 [52] validation_0-error:0.001834 validation_0-logloss:0.041608 validation_1-error:0.002326 validation_1-logloss:0.041807 [53] validation_0-error:0.001834 validation_0-logloss:0.040493 validation_1-error:0.002326 validation_1-logloss:0.040855 [54] validation_0-error:0.001834 validation_0-logloss:0.039457 validation_1-error:0.002326 validation_1-logloss:0.039871 [55] validation_0-error:0.001834 validation_0-logloss:0.038452 validation_1-error:0.002326 validation_1-logloss:0.038755 [56] validation_0-error:0.001834 validation_0-logloss:0.037478 validation_1-error:0.002326 validation_1-logloss:0.037717 [57] validation_0-error:0.001834 validation_0-logloss:0.036439 validation_1-error:0.002326 validation_1-logloss:0.036777 [58] validation_0-error:0.001834 validation_0-logloss:0.035552 validation_1-error:0.002326 validation_1-logloss:0.035936 [59] validation_0-error:0.001834 validation_0-logloss:0.034694 validation_1-error:0.002326 validation_1-logloss:0.034984 [60] validation_0-error:0.001834 validation_0-logloss:0.033826 validation_1-error:0.002326 validation_1-logloss:0.034132 [61] validation_0-error:0.001834 validation_0-logloss:0.032959 validation_1-error:0.002326 validation_1-logloss:0.033348 [62] validation_0-error:0.001834 validation_0-logloss:0.032192 validation_1-error:0.002326 validation_1-logloss:0.032526 [63] validation_0-error:0.001834 validation_0-logloss:0.031476 validation_1-error:0.002326 validation_1-logloss:0.031754 [64] validation_0-error:0.001834 validation_0-logloss:0.030756 validation_1-error:0.002326 validation_1-logloss:0.031081 [65] validation_0-error:0.001834 validation_0-logloss:0.030038 validation_1-error:0.002326 validation_1-logloss:0.030377 [66] validation_0-error:0.001834 validation_0-logloss:0.029332 validation_1-error:0.002326 validation_1-logloss:0.029594 [67] validation_0-error:0.001834 validation_0-logloss:0.028703 validation_1-error:0.002326 validation_1-logloss:0.029079 [68] validation_0-error:0.001834 validation_0-logloss:0.028064 validation_1-error:0.002326 validation_1-logloss:0.028391 [69] validation_0-error:0.001834 validation_0-logloss:0.027404 validation_1-error:0.002326 validation_1-logloss:0.027725 [70] validation_0-error:0.001834 validation_0-logloss:0.026824 validation_1-error:0.002326 validation_1-logloss:0.027187 [71] validation_0-error:0.001834 validation_0-logloss:0.026268 validation_1-error:0.002326 validation_1-logloss:0.026565 [72] validation_0-error:0.001834 validation_0-logloss:0.025679 validation_1-error:0.002326 validation_1-logloss:0.025982 [73] validation_0-error:0.001834 validation_0-logloss:0.025153 validation_1-error:0.002326 validation_1-logloss:0.025413 [74] validation_0-error:0.001834 validation_0-logloss:0.02461 validation_1-error:0.002326 validation_1-logloss:0.024927 [75] validation_0-error:0.001834 validation_0-logloss:0.0241 validation_1-error:0.002326 validation_1-logloss:0.02446 [76] validation_0-error:0.001834 validation_0-logloss:0.023615 validation_1-error:0.002326 validation_1-logloss:0.023921 [77] validation_0-error:0.001834 validation_0-logloss:0.023118 validation_1-error:0.002326 validation_1-logloss:0.023423 [78] validation_0-error:0.001834 validation_0-logloss:0.022671 validation_1-error:0.002326 validation_1-logloss:0.023015 [79] validation_0-error:0.001834 validation_0-logloss:0.022244 validation_1-error:0.002326 validation_1-logloss:0.022538 [80] validation_0-error:0.001834 validation_0-logloss:0.021793 validation_1-error:0.002326 validation_1-logloss:0.022087 [81] validation_0-error:0.001834 validation_0-logloss:0.021396 validation_1-error:0.002326 validation_1-logloss:0.021654 [82] validation_0-error:0.001834 validation_0-logloss:0.020948 validation_1-error:0.002326 validation_1-logloss:0.021198 [83] validation_0-error:0.001834 validation_0-logloss:0.020559 validation_1-error:0.002326 validation_1-logloss:0.020806 [84] validation_0-error:0.001834 validation_0-logloss:0.020144 validation_1-error:0.002326 validation_1-logloss:0.020388 [85] validation_0-error:0.001834 validation_0-logloss:0.019775 validation_1-error:0.002326 validation_1-logloss:0.020057 [86] validation_0-error:0.001834 validation_0-logloss:0.019029 validation_1-error:0.002326 validation_1-logloss:0.019235 [87] validation_0-error:0.001834 validation_0-logloss:0.018672 validation_1-error:0.002326 validation_1-logloss:0.018823 [88] validation_0-error:0.001834 validation_0-logloss:0.018313 validation_1-error:0.002326 validation_1-logloss:0.018507 [89] validation_0-error:0.001834 validation_0-logloss:0.017989 validation_1-error:0.002326 validation_1-logloss:0.01815 [90] validation_0-error:0.001834 validation_0-logloss:0.017376 validation_1-error:0.002326 validation_1-logloss:0.01748 [91] validation_0-error:0.001834 validation_0-logloss:0.017087 validation_1-error:0.002326 validation_1-logloss:0.017189 [92] validation_0-error:0.001834 validation_0-logloss:0.016778 validation_1-error:0.002326 validation_1-logloss:0.016877 [93] validation_0-error:0.001834 validation_0-logloss:0.016458 validation_1-error:0.002326 validation_1-logloss:0.016527 [94] validation_0-error:0.001834 validation_0-logloss:0.015932 validation_1-error:0.002326 validation_1-logloss:0.015956 [95] validation_0-error:0.001834 validation_0-logloss:0.015645 validation_1-error:0.002326 validation_1-logloss:0.015665 [96] validation_0-error:0.001834 validation_0-logloss:0.015379 validation_1-error:0.002326 validation_1-logloss:0.015397 [97] validation_0-error:0.001834 validation_0-logloss:0.015116 validation_1-error:0.002326 validation_1-logloss:0.01513 [98] validation_0-error:0.001834 validation_0-logloss:0.014883 validation_1-error:0.002326 validation_1-logloss:0.014893 [99] validation_0-error:0.001834 validation_0-logloss:0.01464 validation_1-error:0.002326 validation_1-logloss:0.014624【max_depth=2,lr=0.1】Test 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, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.022003, 0.013294, 0.022003, 0.022003, 0.022003, 0.015815, 0.022003, 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, 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0.027404, 0.026824, 0.026268, 0.025679, 0.025153, 0.02461, 0.0241, 0.023615, 0.023118, 0.022671, 0.022244, 0.021793, 0.021396, 0.020948, 0.020559, 0.020144, 0.019775, 0.019029, 0.018672, 0.018313, 0.017989, 0.017376, 0.017087, 0.016778, 0.016458, 0.015932, 0.015645, 0.015379, 0.015116, 0.014883, 0.01464]}, 'validation_1': {'error': [0.048372, 0.042326, 0.048372, 0.042326, 0.042326, 0.042326, 0.023256, 0.042326, 0.042326, 0.023256, 0.006512, 0.017674, 0.017674, 0.017674, 0.017674, 0.017674, 0.017674, 0.017674, 0.024651, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.020465, 0.013488, 0.020465, 0.020465, 0.020465, 0.016279, 0.020465, 0.009302, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 0.002326, 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總結

以上是生活随笔為你收集整理的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)的全部內容，希望文章能夠幫你解決所遇到的問題。

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