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  1. def evaluation(X_train, Y_train, X_test, Y_test, models):
  2.     '''
  3.     Runs test data through all models. Prints confusion matrices and classification reports.
  5.     Parameters: training set and test set, array of models
  6.     Returns: none
  7.     '''
  8.     for name, model in models:
  9.   , Y_train)
  10.             pred = model.predict(X_test)
  11.             print('\n\n\n%s Accuracy: %.2f' % (name, accuracy_score(Y_test, pred)))
  12.             labels = np.unique(Y_test)
  13.             confusion = confusion_matrix(Y_test, pred, labels=labels)
  14.             print('\nConfusion Matrix:')
  15.             print(pd.DataFrame(confusion, index=labels, columns=labels))
  16.             print('\nClassification Report:')
  17.             print(classification_report(Y_test, pred))
  18.     return
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