我的培训和测试图表保持不变,有人能帮我解释一下,或者解释一下我哪里出错了?

我正在做一个简单的机器学习项目。在最初的模型中,我的模型是过拟合的,这是我通过谷歌搜索了解到的,并了解过拟合是什么以及如何检测它。然后,我用SMOTE来减少过度合身,并试图找出它是否仍然过度合身。我得到了一个我无法解释的图表,并尝试了几个链接来了解发生了什么,但失败了。 有谁能告诉我这张图是好是坏?(图片和代码如下所示)

def EF_final(x_train, y_train, x_test, y_test):
  train_scores, test_scores = [], []
  values = [i for i in range(1, 21)]
# evaluate a decision tree for each depth
  for i in values:
    # configure the model
      model_ef = ExtraTreesClassifier(n_estimators = 80, random_state=42, min_samples_split = 2, min_samples_leaf= 1, max_features = 'sqrt', max_depth= 24, bootstrap=False)
    # fit model on the training dataset
      model_ef.fit(x_train, y_train)
    # evaluate on the train dataset
      train_yhat = model_ef.predict(x_train)
      train_acc = accuracy_score(y_train, train_yhat)
      train_scores.append(train_acc)
    # evaluate on the test dataset
      test_yhat = model_ef.predict(x_test)
      test_acc = accuracy_score(y_test, test_yhat)
      test_scores.append(test_acc)
    # summarize progress
      print('>%d, train: %.3f, test: %.3f' % (i, train_acc, test_acc))
# plot of train and test scores vs tree depth
  plt.plot(values, train_scores, '-o', label='Train')
  plt.plot(values, test_scores, '-o', label='Test')
  plt.legend()
  plt.show()