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I've created a binary classifier using K Mean, which predicts fraud and legitimate accounts, 0 and 1. This uses two features, let's say, A and B.
Now, I want to use other features like C and D, to predict fraud and legitimate accounts. Can I use output class predicted in Kmeans, and use it as a label for logistics regression using features C and D?
Sorry, if this sounds like dummy questions, I'm just starting into Data Science, so please let me know if I missed something fundamental.
It is strange to use k-means in addition to logistic regression. Usually k-means is reserved for unsupervised learning problems, this is when you do not have labelled data. Unsupervised learning algorithms are not as powerful and it seems here you have labelled data, thus you should stick to supervised learning techniques.
If you have additional features for your instances then you should include them in your logistic regression.
