Speeding up sklearn logistic regression

Question

I have a model I\'m trying to build using LogisticRegression in sklearn that has a couple thousand features and approximately 60,000 samples. I\'m try

Answer 1

The default solver for LogisticRegressin in sklearn is liblinear which is a suitable solver for normal datasets. For large datasets try the stochastic gradient descent solvers such as sag:

model = LogisticRegression(solver='sag')