title: TypeError: reduction operation 'argmax' not allowed for this dtype
body: <p>I don't actually know what is wrong with my code. Could anyone help?</p>
  
  <pre><code>from sklearn.linear_model import LogisticRegression
  from sklearn.cross_validation import KFold, cross_val_score
  from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report
  
  def printing_Kfold_scores(x_train_data,y_train_data):
      fold = KFold(len(y_train_data),5,shuffle=False) 
  
      # Different C parameters
      c_param_range = [0.01,0.1,1,10,100]
  
      results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])
      results_table['C_parameter'] = c_param_range
  
      # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]
      j = 0
      for c_param in c_param_range:
          print('-------------------------------------------')
          print('C parameter: ', c_param)
          print('-------------------------------------------')
          print('')
  
          recall_accs = []
          for iteration, indices in enumerate(fold,start=1):
  
              # Call the logistic regression model with a certain C parameter
              lr = LogisticRegression(C = c_param, penalty = 'l1')
  
              # Use the training data to fit the model. In this case, we use the portion of the fold to train the model
              # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]
              lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())
  
              # Predict values using the test indices in the training data
              y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)
  
              # Calculate the recall score and append it to a list for recall scores representing the current c_parameter
              recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)
              recall_accs.append(recall_acc)
              print('Iteration ', iteration,': recall score = ', recall_acc)
  
          # The mean value of those recall scores is the metric we want to save and get hold of.
          results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)
          j += 1
          print('')
          print('Mean recall score ', np.mean(recall_accs))
          print('')
  
      best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
  
      # Finally, we can check which C parameter is the best amongst the chosen.
      print('*********************************************************************************')
      print('Best model to choose from cross validation is with C parameter = ', best_c)
      print('*********************************************************************************')
  
      return best_c
  
  best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)
  </code></pre>