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, although the input feature vector has the same structure as the training data.
Also, in order to build the feature vector in the same structure as the training data, I am doing a lot inefficient processing such as adding new empty columns (if data does not exist) and then rearranging the data columns so that it matches with the training structure. Is there a better and cleaner way of formatting the input so that it matches the training structure?
This is the case where the order of column-names while model building is different from order of column-names while model scoring.
I have used the following steps to overcome this error
First load the pickle file
model = pickle.load(open("saved_model_file", "rb"))
extraxt all the columns with order in which they were used
cols_when_model_builds = model.get_booster().feature_names
reorder the pandas dataframe
pd_dataframe = pd_dataframe[cols_when_model_builds]
Try converting data into ndarray before passing it to fit/predict. For eg: if your train data is train_df and test data is test_df. Use below code:
train_x = train_df.values
test_x = test_df.values
Now fit the model:
xgb.fit(train_x,train_y)
Finally, predict:
pred = xgb.predict(test_x)
Hope this helps!
I also had this problem when i used pandas DataFrame (non-sparse representation).
I converted training and testing data into numpy ndarray.
`X_train = X_train.as_matrix()
X_test = X_test.as_matrix()`
This how i got rid of that Error!