ML之LoR&DT&RF：基于LoR&DT(CART)&RF算法對mushrooms蘑菇數據集(22+1,6513+1611)訓練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

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輸出結果

0、數據集

after LabelEncoder

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1、LoR算法

LoR_model_GSCV.grid_scores_： [mean: 0.77012, std: 0.01349, params: {'C': 0.001, 'penalty': 'l1'}, mean: 0.86936, std: 0.01035, params: {'C': 0.001, 'penalty': 'l2'}, mean: 0.91229, std: 0.01022, params: {'C': 0.01, 'penalty': 'l1'}, mean: 0.91045, std: 0.00831, params: {'C': 0.01, 'penalty': 'l2'}, mean: 0.94707, std: 0.00853, params: {'C': 0.1, 'penalty': 'l1'}, mean: 0.93599, std: 0.00841, params: {'C': 0.1, 'penalty': 'l2'}, mean: 0.95984, std: 0.00670, params: {'C': 1, 'penalty': 'l1'}, mean: 0.94953, std: 0.00790, params: {'C': 1, 'penalty': 'l2'}, mean: 0.96553, std: 0.00531, params: {'C': 10, 'penalty': 'l1'}, mean: 0.95722, std: 0.00559, params: {'C': 10, 'penalty': 'l2'}, mean: 0.96646, std: 0.00516, params: {'C': 100, 'penalty': 'l1'}, mean: 0.96599, std: 0.00528, params: {'C': 100, 'penalty': 'l2'}, mean: 0.96661, std: 0.00513, params: {'C': 1000, 'penalty': 'l1'}, mean: 0.96646, std: 0.00564, params: {'C': 1000, 'penalty': 'l2'}] LoR_model_GSCV.best_score_： 0.96661024773042 LoR_model_GSCV.best_params_： {'C': 1000, 'penalty': 'l1'} LoR_model_GSCV.best_score_： 0.96661024773042 LoR_model_GSCV.best_params_： {'C': 1000, 'penalty': 'l1'} LoR_model_GSCV_auc_roc： 0.9739644970414202

2、DT算法

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3、RF算法

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RFC_model_GSCV grid_scores_： [mean: 0.99938, std: 0.00075, params: {'max_features': 'auto', 'min_samples_leaf': 10, 'n_estimators': 10}, mean: 0.99954, std: 0.00070, params: {'max_features': 'auto', 'min_samples_leaf': 10, 'n_estimators': 20},…… mean: 0.97784, std: 0.01071, params: {'max_features': 'log2', 'min_samples_leaf': 80, 'n_estimators': 20}, mean: 0.98215, std: 0.00703, params: {'max_features': 'log2', 'min_samples_leaf': 80, 'n_estimators': 30}, mean: 0.98169, std: 0.00550, params: {'max_features': 'log2', 'min_samples_leaf': 90, 'n_estimators': 80}, mean: 0.98169, std: 0.00801, params: {'max_features': 'log2', 'min_samples_leaf': 90, 'n_estimators': 90}] RFC_model_GSCV best_score_： 0.9998461301738729 RFC_model_GSCV best_params_： {'max_features': 'auto', 'min_samples_leaf': 10, 'n_estimators': 50} RFC_model_GSCV_auc_roc： 1.0

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設計思路

后期更新……

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核心代碼

后期更新……

RF tuned_parameters = {'min_samples_leaf': range(10,100,10), 'n_estimators' : range(10,100,10),'max_features': ['auto','sqrt','log2'] }RFC_model_GSCV = GridSearchCV(RFC_model, tuned_parameters,cv=10) RFC_model_GSCV.fit(X_train,y_train) endtime = time.clock() print ('RFC_model_GSCV Training time：',endtime - starttime) print('RFC_model_GSCV grid_scores_：', RFC_model_GSCV.grid_scores_) print('RFC_model_GSCV best_score_：', RFC_model_GSCV.best_score_) print('RFC_model_GSCV best_params_：', RFC_model_GSCV.best_params_)y_prob = RFC_model_GSCV.predict_proba(X_test)[:,1] y_pred = np.where(y_prob > 0.5, 1, 0) RFC_model_GSCV.score(X_test, y_pred)

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總結

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