# How can I know how 'descriptive' my data is?

I'm an undergrad learning about Statistical/Machine Learning and I've implemented and fitted some models now using methods such as Neural Networks and SVM. I've noticed that when testing some datasets I get more than 95% accuracy (Iris) and others 50% (Wine quality). However here's the thing:

• Iris has 150 samples and Wine has 4989 samples

My intuition says the as $n \rightarrow \infty$ then the function should be easy to fit and the distribution should become clear. However this is a nice counter example. I know I can test for correlation between features and outcomes, or perform some feature selection but I don't know if this answers my question.

So my question really is: Is there any way to quantify a priori how good or descriptive a dataset is?

Of course a priori means without using a model to fit it.