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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 lear

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.

justindomke 5y ago
In higher dimensions, you could do a series of 1d regressions, something like this: 1. Regress x1 against y. Call your curve f1(x1). 2. Regress x2 against (y - f1(x1)). Call your curve f2(x2). 3. Regress x3 against (y - f1(x1) - f2(x2)). Call your curve f3(x3). 4. etc. You'd have to use your computer to read the curves and recompute the residuals each iteration, but there still isn't any algorithmic machine learning. I suspect this would work pretty well, but it would struggle when there are strong interactions between the different input variables.