What is the difference between partial fit and warm start?

Question

Context:

I am using Passive Aggressor from scikit library and confused whether to use warm start or partial fit.

Efforts hitherto

Answer 1

First, let us look at the difference between .fit() and .partial_fit().

.fit() would let you train from the scratch. Hence, you could think of this as a option that can be used only once for a model. If you call .fit() again with a new set of data, the model would be build on the new data and will have no influence of previous dataset.

.partial_fit() would let you update the model with incremental data. Hence, this option can be used more than once for a model. This could be useful, when the whole dataset cannot be loaded into the memory, refer here.

If both .fit() or .partial_fit() are going to be used once, then it makes no difference.

warm_start can be only used in .fit(), it would let you start the learning from the co-eff of previous fit(). Now it might sound similar to the purpose to partial_fit(), but recommended way would be partial_fit(). May be do the partial_fit() with same incremental data few number of times, to improve the learning.