# Difference between sklearn’s “log_loss” and “LogisticRegression”?

I am a newbie currently learning data science from scratch and I have a rather stupid question to ask. I’m currently learning about binary classification, and I understand that the logistic function is a useful tool for this. I looked up the documentation and noticed that there are two logistic related functions I can import, i.e. sklearn.metric.log_loss and sklearn.linear_model.LogisticRegression. When and where should I use them, and what’s the difference?

On a broader note, what’s the difference between a metric and a model, and why is the log loss function a metric? Apologies if this question sounds completely nonsensical, but this is a genuine source of confusion for me!

• I think the answer accepted below is fine, I upvoted it. But since you learn from scratch, maybe a slightly different interpretation can be nice for you. When you are training your model, algorithm tries to minimize your 'cost', which is the sum of the 'losses' or error per sample combined, divided by the number of samples you have. We choose the type of the losses (errors); log_loss is one type as a 'metric'. After training, you get a model that can do predictions, classification/regression/clustering etc. at the end. For 'model' training, you choose your 'metric', depending on your aim. – Ugur MULUK Nov 22 '18 at 11:36