sklearn SGDClassifier fit() vs partial_fit()
<p>I am confused about <code>fit()</code> and <code>partial_fit()</code> method of <code>SGDClassifier</code>. Documentation says for both, "Fit linear model with Stochastic Gradient Descent.".</p>

<p>What I know about stochastic gradient descent is, it takes one (or a fraction of whole) training example to update parameters of model in one iteration. And gradient descent uses whole data set in each iteration. I want to train a model using logistic regression. I want to implement normal Gradient Descent and Stochastic gradient descent and compare time required for them. How to do that with <code>SGDClassifier</code>? Does <code>fit()</code> method works as normal gradient descent? </p>