How to combine multiple feature sets in bag of words

Question

I have text classification data with predictions depending on categories, 'descriptions' and 'components'. I could do the classification using bag of words in python with scikit on 'descriptions'. But I want to get predictions using both categories in bag of words with weights to individual feature sets x = descriptions + 2* components How should I proceed?

Answer 1

You can train individual classifiers for descriptions and merchants, and obtain a final score using score = w1 * predictions + w2 * components.

The values of w1 and w2 should be obtained using cross validation.

Alternatively, you can train a single multiclass classifier by combining the training dataset.

You will now have 4 classes:

1. Neither 'predictions' nor 'components'
2. 'predictions' but not 'components'
3. not 'predictions' but 'components'
4. 'predictions' and 'components'

And you can go ahead and train as usual.