GridSearchCV - FitFailedWarning：估计器拟合失败

Question

我正在运行这个：

# Hyperparameter tuning - Random Forest #

# Hyperparameters' grid
parameters = {'n_estimators': list(range(100, 250, 25)), 'criterion': ['gini', 'entropy'], 
              'max_depth': list(range(2, 11, 2)), 'max_features': [0.1, 0.2, 0.3, 0.4, 0.5], 
              'class_weight': [{0: 1, 1: i} for i in np.arange(1, 4, 0.2).tolist()], 'min_samples_split': list(range(2, 7))}


# Instantiate random forest
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(random_state=0)


# Execute grid search and retrieve the best classifier
from sklearn.model_selection import GridSearchCV
classifiers_grid = GridSearchCV(estimator=classifier, param_grid=parameters, scoring='balanced_accuracy',
                                   cv=5, refit=True, n_jobs=-1)
classifiers_grid.fit(X, y)

我收到此警告：

.../anaconda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: 
FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
TypeError: '<' not supported between instances of 'str' and 'int'

这是为什么？我该如何解决它？

Answer 1

我有类似的 FitFailedWarning 问题，但细节不同，经过多次运行后发现，参数值传递有错误，请尝试

parameters = {'n_estimators': [100,125,150,175,200,225,250], 
              'criterion': ['gini', 'entropy'], 
              'max_depth': [2,4,6,8,10], 
              'max_features': [0.1, 0.2, 0.3, 0.4, 0.5], 
              'class_weight': [0.2,0.4,0.6,0.8,1.0],               
              'min_samples_split': [2,3,4,5,6,7]}

这肯定会通过，对我来说，它发生在 XGBClassifier 中，不知何故，值数据类型混合在一起

还有一个就是如果数值超出范围，比如XGBClassifier的subsample参数最大值是1.0，如果设置为1.1，就会出现FitFailedWarning