That's really cool! I really liked the method for drawing and averaging predictions.

One point though:

> What’s the point of this? It’s just that machine learning isn’t magic. For simple problems, it doesn’t fundamentally give you anything better than you can get just from common sense.

I think that really depends on the dimensionality of the data and how many truly important features there are. All the examples have a single feature (and single target which is common), so it's easy to visualize in 2d. Two input features are still feasible, though it's harder to draw a manifold for the predictions, but personally for 3+ features I think it would be really tricky. You could split it to all possible pairs and draw predictions for each pair, but this way you could miss important interactions between features.