ValueError: feature_names mismatch: in xgboost in the predict() function

Question

I have trained an XGBoostRegressor model. When I have to use this trained model for predicting for a new input, the predict() function throws a feature_names mismatch error,

Answer 1

I'm contributing an answer as I experienced this problem when putting a fitted XGBRegressor model into production. Thus, this is a solution for cases where you cannot select column names from a y training or testing DataFrame, though there may be cross-over which could be helpful.

The model had been fit on a Pandas DataFrame, and I was attempting to pass a single row of values as a np.array to the predict function. Processing the values of the array had already been performed (reverse label encoded, etc.), and the array was all numeric values.

I got the familiar error:

ValueError: feature_names mismatch followed by a list of the features, followed by a list of the same length: ['f0', 'f1' ....]

While there are no doubt more direct solutions, I had little time and this fixed the problem:

1. Make the input vector a Pandas Dataframe:

series = {'feature1': [value],
          'feature2': [value],
          'feature3': [value],
          'feature4': [value],
          'feature5': [value],
          'feature6': [value],
          'feature7': [value],
          'feature8': [value],
          'feature9': [value],
          'feature10': [value]
           }

self.vector = pd.DataFrame(series)

2. Get the feature names that the trained model knows:

names = model.get_booster().feature_names

3. Select those feature from the input vector DataFrame (defined above), and perform iloc indexing:

result = model.predict(vector[names].iloc[[-1]])

---

The iloc transformation I found here.

Selecting the feature names – as models in the Scikit Learn implementation do not have a feature_names attribute – using get_booster( ).feature_names I found in @Athar post above.

Check out the the documentation to learn more.

Hope this helps.