# 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?

• No. Wrong. Simple search in this site would give you suggestions. Oct 26, 2017 at 1:25
• This stats.stackexchange.com/questions/19048/… is a pretty good explanation. Oct 26, 2017 at 4:18

• by ground truths you mean what you're trying to predict ? Oct 26, 2017 at 3:07