• 使用网格搜索法对5个模型进行调优（调参时采用五折交叉验证的方式），并进行模型评估，记得展示代码的运行结果。 时间：2天

1.利用GGridSearchCV调参

1.1参数选择

首先选择5个模型要调的参数，这里是根据以前在知乎看的一张图片（感谢大佬！）

parameters_log = {‘C‘:[0.001,0.01,0.1,1,10]}
parameters_svc = {‘C‘:[0.001,0.01,0.1,1,10]}  #这两个模型本来分数就不行，就少选择写参数来搜索
parameters_tree = {‘max_depth‘:[5,8,15,25,30,None],‘min_samples_leaf‘:[1,2,5,10], ‘min_samples_split‘:[2,5,10,15]}
parameters_forest = {‘max_depth‘:[5,8,15,25,30,None],‘min_samples_leaf‘:[1,2,5,10],
                     ‘min_samples_split‘:[2,5,10,15],‘n_estimators‘:[7,8,9,10]} #这两个模型过拟合很厉害，参数多点
parameters_xgb = {‘gamma‘:[0,0.05,0.1,0.3,0.5],‘learning_rate‘:[0.01,0.015,0.025,0.05,0.1],
                  ‘max_depth‘:[3,5,7,9],‘reg_alpha‘:[0,0.1,0.5,1.0]}  #这个模型表现挺好，多调试一点

parameters_total = {‘log_clf‘:parameters_log,‘svc_clf‘:parameters_svc,‘tree_clf‘:parameters_tree,
                    ‘forest_clf‘:parameters_forest,‘xgb_clf‘:parameters_xgb}

1.2划分验证集

本来想用sklearn的模块划分的，但是好像不能传入数组，就是手动划分前1000个样本

X_val = X_train_scaled[:1000]
y_val = y_train[:1000]

1.3模型用字典集合

from sklearn.model_selection import GridSearchCV
def gridsearch(X_val,y_val,models,parameters_total):
    models_grid = {}
    for model in models:
        grid_search = GridSearchCV(models[model],param_grid=parameters_total[model],n_jobs=-1,cv=5,verbose=10)
        grid_search.fit(X_val,y_val)
        models_grid[model] = grid_search.best_estimator_
    return models_grid

1.4查看参数

models_grid

{‘log_clf‘: LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
           intercept_scaling=1, max_iter=100, multi_class=‘warn‘,
           n_jobs=None, penalty=‘l2‘, random_state=None, solver=‘warn‘,
           tol=0.0001, verbose=0, warm_start=False),
 ‘svc_clf‘: SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,
   decision_function_shape=‘ovr‘, degree=3, gamma=‘auto_deprecated‘,
   kernel=‘rbf‘, max_iter=-1, probability=False, random_state=None,
   shrinking=True, tol=0.001, verbose=False),
 ‘tree_clf‘: DecisionTreeClassifier(class_weight=None, criterion=‘gini‘, max_depth=5,
             max_features=None, max_leaf_nodes=None,
             min_impurity_decrease=0.0, min_impurity_split=None,
             min_samples_leaf=5, min_samples_split=2,
             min_weight_fraction_leaf=0.0, presort=False, random_state=None,
             splitter=‘best‘),
 ‘forest_clf‘: RandomForestClassifier(bootstrap=True, class_weight=None, criterion=‘gini‘,
             max_depth=15, max_features=‘auto‘, max_leaf_nodes=None,
             min_impurity_decrease=0.0, min_impurity_split=None,
             min_samples_leaf=10, min_samples_split=2,
             min_weight_fraction_leaf=0.0, n_estimators=7, n_jobs=None,
             oob_score=False, random_state=None, verbose=0,
             warm_start=False),
 ‘xgb_clf‘: XGBClassifier(base_score=0.5, booster=‘gbtree‘, colsample_bylevel=1,
        colsample_bytree=1, gamma=0.5, learning_rate=0.05, max_delta_step=0,
        max_depth=5, min_child_weight=1, missing=None, n_estimators=100,
        n_jobs=1, nthread=None, objective=‘binary:logistic‘, random_state=0,
        reg_alpha=1.0, reg_lambda=1, scale_pos_weight=1, seed=None,
        silent=True, subsample=1)}

2.参数优化前后对比

models_grid = gridsearch(X_val,y_val,models,parameters_total)
results_test_grid,results_train_grid = metrics(models_grid,X_train_scaled,X_test_scaled,y_train,y_test)

左边优化前，右边优化后

训练集上：

测试集上：

可以看到明显的防止了树模型的过拟合，但是其他评估数据提升不是很大！！

看一下ROC曲线对比

左边优化前，右边优化后

原文地址：https://www.cnblogs.com/Hero1best/p/10891398.html