我有一个脚本，它应该使用交叉验证来训练不同的模型，然后计算平均准确率，以便我可以使用最佳模型进行分类任务。但是，我对每个分类器得到的结果都是相同的。

结果看起来像这样：

---Filename in processed................ corpusAmazon_trainetiquette  : [0 1]Embeddings bert model used.................... :  smModel name: Model_LSVC_ovr------------cross val predict used---------------- accuracy with cross_val_predict : 0.6582974014576258corpusAmazon_train file terminated--- ---------------cross val score used ----------------------- [0.66348722 0.66234262 0.63334605 0.66959176 0.66081648 0.6463182 0.66730256 0.65572519 0.65648855 0.66755725]0.66 accuracy with a standard deviation of 0.01 Model name: Model_G_NB------------cross val predict used---------------- accuracy with cross_val_predict : 0.6582974014576258corpusAmazon_train file terminated--- ---------------cross val score used ----------------------- [0.66348722 0.66234262 0.63334605 0.66959176 0.66081648 0.6463182 0.66730256 0.65572519 0.65648855 0.66755725]0.66 accuracy with a standard deviation of 0.01 Model name: Model_LR------------cross val predict used---------------- accuracy with cross_val_predict : 0.6582974014576258corpusAmazon_train file terminated--- ---------------cross val score used ----------------------- [0.66348722 0.66234262 0.63334605 0.66959176 0.66081648 0.6463182 0.66730256 0.65572519 0.65648855 0.66755725]0.66 accuracy with a standard deviation of 0.01 

使用交叉验证的代码行：

models_list = {'Model_LSVC_ovr': model1, 'Model_G_NB': model2, 'Model_LR': model3, 'Model_RF': model4, 'Model_KN': model5, 'Model_MLP': model6, 'Model_LDA': model7, 'Model_XGB': model8}# cross_validationdef cross_validation(features, ylabels, models_list, n, lge_model):    cv_splitter = KFold(n_splits=10, shuffle=True, random_state=42)    features, s = get_flaubert_layer(features, lge_model)    for model_name, model in models_list.items():        print("Model name: {}".format(model_name))        print("------------cross val predict used----------------", "\n")        y_pred = cross_val_predict(model, features, ylabels, cv=cv_splitter, verbose=1)        accuracy_score_predict = accuracy_score(ylabels, y_pred)        print("accuracy with cross_val_predict :", accuracy_score_predict)        print("---------------cross val score used -----------------------", "\n")        scores = cross_val_score(model, features, ylabels, scoring='accuracy', cv=cv_splitter)        print("%0.2f accuracy with a standard deviation of %0.2f" % (accuracy_score_mean, accuracy_score_std), "\n")

即使使用cross_val_score，每个模型的准确率也相同。你有任何想法吗？可能是由于我在交叉验证函数中使用了random_state吗？

模型定义的代码：

def classifiers_b():    model1 = LinearSVC()    model2 = GaussianNB()  # MultinomialNB() X cannot be a non-negative    model3 = LogisticRegression()    model4 = RandomForestClassifier()    model5 = KNeighborsClassifier()    model6 = MLPClassifier(hidden_layer_sizes=(50, 100, 50), max_iter=500, activation='relu', solver='adam',                           random_state=1)    model8 = XGBClassifier(eval_metric="logloss")    model7 = LinearDiscriminantAnalysis()    #models_list = {'Model_LSVC_ovr': model1, 'Model_G_NB': model2, 'Model_LR': model3, 'Model_RF': model4, 'Model_KN': model5, 'Model_MLP': model6, 'Model_LDA': model7, 'Model_XGB': model8}

回答：

我建议为每个模型使用一个管道。看起来你在每次迭代中对同一个模型进行CV。你可以查看这里的文档，了解如何使用它们。然后为每个模型管道执行CV。