ML之LoR&DT&RF：基于LoR&DT(CART)&RF算法对mushrooms蘑菇数据集(22+1,6513+1611)训练来预测蘑菇是否毒性(二分类预测)
输出结果
0、数据集
after LabelEncoder
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
3、RF算法
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
设计思路
后期更新……
核心代码
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)
