I have read that retraining a model depends highly on what you are trying to achieve. I am conscious that maybe I need to retrain my model daily and after a certain time I have to train the model again from scratch. That's no problem. The thing is, if I were to retrain a K-Modes model, I would need the previous centroids saved. I think this is also not a problem. Seeing the implementation of K-Modes here, the parameter
init can be
Cao. In the K-Means implementation of sklearn (see here), we can pass a
ndarray of shape
(n_clusters, n_features). I can't find this feature in the K-Modes implementation. So my specific questions are:
- Is saving the previous centroids sufficient to retrain the model?
- Is there a way to pass previous centroids in the K-Modes implementation I have mentioned? If not, should I implement my own K-Modes?