这是我的代码

param_grid = [{'kneighborsclassifier__n_neighbors': [3, 4, 5, 6, 7]},               {'svc__C': [0.1, 1, 10, 100], 'svc__gamma': [0.001, 0.01, 0.1, 1, 10]},              {'randomforestclassifier__n_estimators': [50, 100, 200, 300 ,400]},              {'decisiontreeclassifier__max_depth': [12, 25, 50, 75, 100]},              {'adaboostclassifier__n_estimators': [50, 100, 200, 300 ,400]},              {'baggingclassifier__n_estimators': [50, 100, 200, 300, 400]}             ]inner_cv = StratifiedShuffleSplit(test_size=.8, train_size=.2, n_splits=5, random_state=0)outer_cv = StratifiedShuffleSplit(test_size=.8, train_size=.2, n_splits=5, random_state=0)models = [knn, svc, forest, dtc, ada, bag]model_names = ['knn', 'svc','forest', 'dtc', 'ada', 'bag']for m, mname in zip(models, model_names):    pipe = make_pipeline(VarianceThreshold(threshold=1),                          MinMaxScaler(),                         SelectKBest(f_classif, k=20),                           m)    grid = GridSearchCV(pipe, param_grid=param_grid, cv=inner_cv)    grid.fit(X_train_test, y_train_test)    nested_score = cross_val_score(grid, X=X_train_test, y=y_train_test.values.ravel(), cv=outer_cv)    print(mname)    print(grid.best_params_)    print(grid.best_score_)    print('\n')

这是错误信息：

ValueError: Invalid parameter svc for estimator Pipeline(memory=None,         steps=[('variancethreshold', VarianceThreshold(threshold=1)),                ('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))),                ('selectkbest',                 SelectKBest(k=20,                             score_func=<function f_classif at 0x0000019E0A485AF8>)),                ('kneighborsclassifier',                 KNeighborsClassifier(algorithm='auto', leaf_size=30,                                      metric='minkowski', metric_params=None,                                      n_jobs=None, n_neighbors=5, p=2,                                      weights='uniform'))],         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

我不知道哪里出了问题。我是从管道的named_steps中复制了参数和模型名称。如果我没有使用参数网格运行它，它可以工作，所以问题很可能出在参数网格上。

回答：

这样似乎可以工作，但我不是很喜欢这种方式。

pg1 = {'kneighborsclassifier__n_neighbors': [3, 4, 5, 6, 7]}pg2 = {'svc__C': [0.1, 1, 10, 100],       'svc__gamma': [0.001, 0.01, 0.1, 1, 10]}pg3 = {'randomforestclassifier__n_estimators': [50, 100, 200, 300 ,400]}pg4 = {'decisiontreeclassifier__max_depth': [12, 25, 50, 75, 100]}pg5 = {'adaboostclassifier__n_estimators': [50, 100, 200, 300 ,400]}pg6 = {'baggingclassifier__n_estimators': [50, 100, 200, 300, 400]}param_grid_list = [pg1, pg2, pg3, pg4, pg5, pg6]

然后循环变为：

for m, p, mname in zip(models, param_grid_list, model_names):    pipe = make_pipeline(VarianceThreshold(threshold=1),                          MinMaxScaler(),                         SelectKBest(f_classif, k=20),                           m)    grid = GridSearchCV(pipe, param_grid=p, cv=inner_cv)    grid.fit(X_train_test, y_train_test)    nested_score = cross_val_score(grid, X=X_train_test, y=y_train_test.values.ravel(), cv=outer_cv)    print(mname)    print(grid.best_params_)    print(grid.best_score_)    print('\n')