XGBoost difference in train and test features after converting to DMatrix

Question

Just wondering how is possible next case:

 def fit(self, train, target):
     xgtrain = xgb.DMatrix(train, label=target, missing=np.nan)
     self.model = xg         


        

Answer 1

Such an issue occurred for me when RandomUnderSampler (RUS) method returned a np.array rather than a Pandas DataFrame with column names.

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(return_indices=True)
X_rus, y_rus, id_rus = rus.fit_sample(X_train, y_train)

I resolved the issue with this:

X_rus = pd.DataFrame(X_rus, columns = X_train.columns)

Basically taking the output of RUS method and creating a Pandas DataFrame out of it with column names from the original X_train data which was the input of RUS method.

This can be generalized to any similar problem where XGBoost expected to read column names but could not. Just create a Pandas DataFrame and assign the column names accordingly.