I've been reading up on machine leaning and keep seeing data sets split up into a
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?