Python ValueError：模型的特征数必须与输入匹配。在随机林中，模型n_特征为10，输入n_特征为7

我试图建立一个预测模型，但我得到了以下错误。我怎么把这个修好

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ValueError                                Traceback (most recent call last)
<ipython-input-11-cd63269e2c86> in <module>
      8 
      9 # Make predictions on test data using the model trained on original data
---> 10 baseline_predictions = rf.predict(original_test_features)
     11 
     12 # Performance metrics

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\ensemble\forest.py in predict(self, X)
    691         check_is_fitted(self, 'estimators_')
    692         # Check data
--> 693         X = self._validate_X_predict(X)
    694 
    695         # Assign chunk of trees to jobs

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\ensemble\forest.py in _validate_X_predict(self, X)
    357                                  "call `fit` before exploiting the model.")
    358 
--> 359         return self.estimators_[0]._validate_X_predict(X, check_input=True)
    360 
    361     @property

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\tree\tree.py in _validate_X_predict(self, X, check_input)
    400                              "match the input. Model n_features is %s and "
    401                              "input n_features is %s "
--> 402                              % (self.n_features_, n_features))
    403 
    404         return X

ValueError: Number of features of the model must match the input. Model n_features is 10 and input n_features is 7 

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我试图用随机森林分类器为某一组数据建立一个预测模型。但是，我收到一条错误消息，指出模型的数字特征与输入不匹配

您的模型已使用具有10个特征的数据集进行训练，现在您的预测集（即

原始测试特征

仅具有7个特征）。这就是为什么会出现功能不匹配错误。请检查

原始\u测试\u功能

，确保所有功能都与模型训练集匹配。

我正在尝试调整参数，如果不调整，模型精度将保持不变。。。原始特征索引=[特征列表。如果特征不在['feature1'、'feature2'、'feature3']]中，则特征列表中特征的索引（特征）]我可以用其他方法进行参数调整吗？？

# Find the original feature indices 
original_feature_indices = [feature_list.index(feature) for feature in
                                      feature_list if feature not in
                                      ['feature1', 'feature2', 'feature3']]

# Create a test set of the original features
original_test_features = test_features[:, original_feature_indices]

# Make predictions on test data using the model trained on original data
baseline_predictions = rf.predict(original_test_features)

# Performance metrics
baseline_errors = abs(baseline_predictions - test_labels)

print('Metrics for Random Forest Trained on Original Data')
print('Average absolute error:', round(np.mean(baseline_errors), 2), 'degrees.')

# Calculate mean absolute percentage error (MAPE)
baseline_mape = 100 * np.mean((baseline_errors / test_labels))

# Calculate and display accuracy
baseline_accuracy = 100 - baseline_mape
print('Accuracy:', round(baseline_accuracy, 2), '%.')