code: from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score, confusion_matrix
  import pandas as pd
  
  def getClassification_scores(true_classes, predicted_classes):
      acc = accuracy_score(true_classes, predicted_classes)
      prec = precision_score(true_classes, predicted_classes,average="macro")
      f1 = f1_score(true_classes, predicted_classes,average="macro")
      recall = recall_score(true_classes, predicted_classes,average="macro")
      scores = [acc, prec, f1, recall]
      return scores
  
  
  def evaluation_Step(mean_pred_probs):
      scores = getClassification_scores(mean_pred_probs['label'], mean_pred_probs['pred_class'])
      acc = scores[0]
      prec = scores[1]
      f1 = scores[2]
      recall = scores[3]
      
      print('Scores:')
      print('Accuracy: %f' % acc)
      print('Precision: %f' % prec)
      print('F1-score: %f' % f1)
      print('Recall: %f' % recall)
            
  
      #confusion matrix
      conf_mat_df = pd.crosstab(mean_pred_probs['label'], mean_pred_probs['pred_class'], margins=True)
      
      print('\nConfusion matrix')
      print(conf_mat_df)
      return
  
  
  
apis: pandas.crosstab sklearn.metrics.f1_score sklearn.metrics.accuracy_score sklearn.metrics.precision_score sklearn.metrics.recall_score