How to make stochastic gradient regressor run up to 1000 epochs or yield better results?

Question

I am running the stochastic gradient regressor from sklearn (docs).

Here are the parameters I used:

 {loss: "huber", 
    "learning_rate": "adaptive", 
    "penalty": "l1", 
    "alpha": "0.001", 
    "l1_ratio": "0.75", 
    "early_stopping": "True", 
    "max_iter": "2000", 
    "n_iter_no_change": "15", 
    "validation_fraction": "0.1", 
    "warm_start": "True", 
    "tol": "0.0001", "random_state": "1"}

Unfortunately my epoch does not reach 2000. I understand I set that if it does change after 15 runs, it should terminate, how can I get better with the stochastic gradient? because the final validation are not very impressive.

   -- Epoch 38
    Norm: 38.43, NNZs: 218, Bias: 6.923232, T: 2062792, Avg. loss: 0.119096

Answer 1

From the parameters shown, it is apparent that you call SGDRegressor with early_stopping=True. You should change it to early_stopping=False (or omit the argument altogether, since its default value is indeed False - see the docs).