Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Are my data points separable?
That’s the million-dollar question! In the example above, we can clearly see
that data points in the validation set are more separable than those in the
training set.
What happens if the points are not separable at all? Let’s take a quick detour
and look at another tiny dataset with 10 data points, seven red, three blue.
The colors are the labels (y), and each data point has a single feature (x 1 ). We
could plot them along a line; after all, we have only one dimension.
Can you separate the blue points from the red ones with one straight line?
Obviously not—these points are not separable (in one dimension, that is).
Should we give up, then?
"Never give up, never surrender!"
Commander Taggart
If it doesn’t work in one dimension, try using two! There is just one problem,
though: Where does the other dimension come from? We can use a trick
here: We apply a function to the original dimension (feature) and use the
result as a second dimension (feature). Quite simple, right?
For the tiny dataset at hand, we could try the square function:
What does it look like?
Decision Boundary | 239