title: sklearn cross_val_score() returns NaN values
body: <p>i'm trying to predict next customer purchase to my job. I followed a guide, but when i tried to use cross_val_score() function, it returns NaN values.<a href="https://i.stack.imgur.com/AHGzs.png" rel="noreferrer">Google Colab notebook screenshot</a></p>
  
  <p>Variables: </p>
  
  <ul>
  <li>X_train is a dataframe</li>
  <li>X_test is a dataframe</li>
  <li>y_train is a list</li>
  <li>y_test is a list</li>
  </ul>
  
  <p>Code:</p>
  
  <pre><code>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)
  X_train = X_train.reset_index(drop=True)
  X_train
  X_test = X_test.reset_index(drop=True)
  
  y_train = y_train.astype('float')
  y_test = y_test.astype('float')
  
  models = []
  models.append(("LR",LogisticRegression()))
  models.append(("NB",GaussianNB()))
  models.append(("RF",RandomForestClassifier()))
  models.append(("SVC",SVC()))
  models.append(("Dtree",DecisionTreeClassifier()))
  models.append(("XGB",xgb.XGBClassifier()))
  models.append(("KNN",KNeighborsClassifier()))´
  
  for name,model in models:
     kfold = KFold(n_splits=2, random_state=22)
     cv_result = cross_val_score(model,X_train,y_train, cv = kfold,scoring = "accuracy")
     print(name, cv_result)
  &gt;&gt;
  LR [nan nan]
  NB [nan nan]
  RF [nan nan]
  SVC [nan nan]
  Dtree [nan nan]
  XGB [nan nan]
  KNN [nan nan]
  </code></pre>
  
  <p>help me please!</p>