I'm trying to use SGD to classify a large dataset. As the data is too large to fit into memory, I'd like to use the partial_fit method to train the classifier. I have selected a sample of the dataset (100,000 rows) that fits into memory to test fit vs. partial_fit:

from sklearn.linear_model import SGDClassifier

def batches(l, n):

for i in xrange(0, len(l), n):

yield l[i:i+n]

clf1 = SGDClassifier(shuffle=True, loss='log')

clf1.fit(X, Y)

clf2 = SGDClassifier(shuffle=True, loss='log')

n_iter = 60

for n in range(n_iter):

for batch in batches(range(len(X)), 10000):

clf2.partial_fit(X[batch[0]:batch[-1]+1], Y[batch[0]:batch[-1]+1], classes=numpy.unique(Y))

I then test both classifiers with an identical test set. In the first case I get an accuracy of 100%. As I understand it, SGD by default passes 5 times over the training data (n_iter = 5).

In the second case, I have to pass 60 times over the data to reach the same accuracy.

Why this difference (5 vs. 60)? Or am I doing something wrong?