我试图使用遗传算法通过TPOT来优化随机森林的超参数。我遇到了一个错误，并且不太确定如何解决。以下是我使用的主要代码。

from sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import RandomizedSearchCVfrom sklearn.model_selection import train_test_splitfrom tpot import TPOTClassifierX = my_df_featuresy = my_df_targetX_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)model_parameters = {'n_estimators': [100,200],                      "max_depth" : [None, 5, 10],                      "max_features" : [10]}  # 当我运行这段代码时，它似乎运行得很好# model_tuned = GridSearchCV(RandomForestClassifier(),model_parameters, cv=5)# 但这似乎不起作用 model_tuned = TPOTClassifier(generations= 2, population_size= 2, offspring_size= 2,                                      verbosity= 2, early_stop= 10,                                      config_dict=                                      {'sklearn.ensemble.RandomForestClassifier': model_parameters},                                       cv = 5) model_tuned.fit(X_train,y_train)

当使用TPOT（而不是RandomForest）时，上面的最后一行会产生以下错误：

ValueError: cannot set using a slice indexer with a different length than the value"

回答：

我尝试使用iris数据集运行TPOT，没有出现任何错误

from sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import RandomizedSearchCVfrom sklearn.model_selection import train_test_splitfrom tpot import TPOTClassifierfrom sklearn import datasetsiris = datasets.load_iris()X = iris.data y = iris.targetX_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)model_parameters = {'n_estimators': [100,200],                      "max_depth" : [None, 5, 10],                      "max_features" : [len(X_train[0])]}  model_tuned = TPOTClassifier(generations= 2,                              population_size= 2,                              offspring_size= 2,                             verbosity= 2,                              early_stop= 10,                              config_dict={'sklearn.ensemble.RandomForestClassifier':                              model_parameters},                              cv = 5) model_tuned.fit(X_train,y_train)

我认为你的数据集的形状或类型可能有问题

可能是由于你使用了pandas DataFrame

尝试这样做：

X = X.to_numpyy = y.to_numpy