# Difference between training & test set

I've been reading up on machine leaning and keep seeing data sets split up into a training, test, & validation set. Here's what I think the differences are based on what I've read:

training set => choosing the features that you think are most important in predicting your label

test set => splitting your data into training & testing sets (e.g. 75% of your features used to predict labels)

validation set => new, real world data never been seen before

Are these distinctions accurate?

• by ground truths you mean what you're trying to predict ? – e1v1s Oct 26 '17 at 3:07