# I did PCA of my dataset with two classes and here is the scatterplot; how can I tell if my dataset is learnable?

What should I look for in my PCA?

I'm doing supervised learning with (unfortunately only) 2000 examples, evenly split into 1000 yes and 1000 no. Each vector is a 1000 dimensional boolean vector. I have not run mean normalization or other normalizations on the data set.

I ran PCA and got the following. For the first three dimensions, I got:

$$\text{explained variance ratio} = \left[ \texttt{0.0193} \; \texttt{0.00933} \; \texttt{0.00817} \right]$$

I have a video in 3D of the plot here, but for simplicity, here the data set is post PCA in 2 dimensions (red means yes, blue means no).

Question: Does this dataset seem to be non-learnable? How can I tell?

• Lot's of people do it but I'm not sure PCA is the best method to apply to binary data. You are also throwing away much information with only 3 PCs, try other techniques if possible and also regularized learners. Perhaps your data is also sparse, and you could try methods tailored to that kind of data. – Firebug May 29 '16 at 17:03