通常，我们使用GridSearchCV对单个模型的超参数进行网格搜索，例如：
model_ada = AdaBoostClassifier()
params_ada = {'n_estimators':[10,20,30,50,100,500,1000], 'learning_rate':[0.5,1,2,5,10]}
grid_ada = GridSearchCV(estimator = model_ada, param_grid = params_ada, scoring = 'accuracy', cv = 5, verbose = 1, n_jobs = -1)
grid_ada.fit(X_train, y_train)有没有什么技术或函数可以让我们对机器学习模型本身进行网格搜索？例如，我想做如下操作：
models = {'model_gbm':GradientBoostingClassifier(), 'model_rf':RandomForestClassifier(), 'model_dt':DecisionTreeClassifier(), 'model_svm':SVC(), 'model_ada':AdaBoostClassifier()}
params_gbm = {'learning_rate':[0.1,0.2,0.3,0.4], 'n_estimators':[50,100,500,1000,2000]}
params_rf = {'n_estimators':[50,100,500,1000,2000]}
params_dt = {'splitter':['best','random'], 'max_depth':[1, 5, 10, 50, 100]}
params_svm = {'C':[1,2,5,10,50,100,500], 'kernel':['rbf','poly','sigmoid','linear']}
params_ada = {'n_estimators':[10,20,30,50,100,500,1000], 'learning_rate':[0.5,1,2,5,10]}
params = {'params_gbm':params_gbm, 'params_rf':params_rf, 'params_dt':params_dt, 'params_svm':params_svm, 'params_ada':params_ada}
grid_ml = "that function"(models = models, params = params)
grid_ml.fit(X_train, y_train)其中”that function”是我需要使用的执行此类操作的函数。
回答：
我曾经遇到过类似的问题，但没有找到预定义的包/方法可以实现这一点。因此，我编写了自己的函数来实现这个功能：
def Algo_search(models , params):
max_score = 0
max_model = None
max_model_params = None
for i,j in zip(models.keys() , models.values() ):
gs = GridSearchCV(estimator=j,param_grid=params[i])
a = gs.fit(X_train,y_train)
score = gs.score(X_test,y_test)
if score > max_score:
max_score = score
max_model = gs.best_estimator_
max_model_params = gs.best_params_
return max_score, max_model, max_model_params
#数据点
models = {'model_gbm':GradientBoostingClassifier(), 'model_rf':RandomForestClassifier(),
'model_dt':DecisionTreeClassifier(), 'model_svm':SVC(), 'model_ada':AdaBoostClassifier()}
params_gbm = {'learning_rate':[0.1,0.2,0.3,0.4], 'n_estimators':[50,100,500,1000,2000]}
params_rf = {'n_estimators':[50,100,500,1000,2000]}
params_dt = {'splitter':['best','random'], 'max_depth':[1, 5, 10, 50, 100]}
params_svm = {'C':[1,2,5,10,50,100,500], 'kernel':['rbf','poly','sigmoid','linear']}
params_ada = {'n_estimators':[10,20,30,50,100,500,1000], 'learning_rate':[0.5,1,2,5,10]}
params = {'model_gbm':params_gbm, 'model_rf':params_rf, 'model_dt':params_dt, 'model_svm':params_svm, 'model_ada':params_ada}
grid_ml = Algo_search(models = models, params = params)