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I have a training data set that has categorical features on which I use pd.get_dummies to one hot encode. This produces a data set with n features. I then train a classification model on this data set with n features. If I now get some new data with the same categorical features and again perform one hot encoding, the resultant number of features is m < n.

I cannot predict the classes of the new data set if the dimensions don't match with the original training data.

Is there a way to include all of the original n features in the new data set after one hot encoding?

EDIT: I am using sklearn.ensemble.RandomForestClassifier as my classification library.

PyRsquared
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1 Answers1

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For example ,

You have tradf with column ['A_1','A_2']

With your new df you have column['A'] but only have one category 1 , you can do 

pd.get_dummies(df).reindex(columns=tradf.columns,fill_value=0)
BENY
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