What exactly is machine learning and how does it work?
In: Technology
Machine Learning is using known data, feeding it into some sort of classifier that has its parameters tweaked to match that input. Then when you feed it in new data if you didn’t mess up it will be able to classify that new data with a decent degree of accuracy even if it hasn’t been trained on that specific data.
Maybe that classifier is classifying pictures as cats or not cats. Maybe it’s driving a robot car and taking in video and deciding to turn left or right, accelerate or brake, etc.
As for how it works, here’s an example of a relatively simple but powerful classification algorithm.
So say you have lots and red and blue dots on a vertical line. You want to draw a point to separate them as best as you can. You want to do this because there is a pattern to what color the dots are based on position, and when new dots appear you want to figure out if they are probably red or probably blue based on that position.
So say there’s more red dots up top in general. You pick a point, count the dots on both sides. Shift the point up and count again. Shift the point down and count again. One of those directions is a bit better, say up, so you keep shifting the point up till it gets worse. Then you turn around and backtrack slowly till it gets better. Eventually you decide that you are barely moving the point and it’s good enough.
Now you know that if a dot appears above that point it’s probably red, or if its below it’s probably blue.
Of course we aren’t really sorting dots. Instead think of the vertical position as representative of some property like… your blood pressure, and the color red as people who have heart attacks before 60 or something.
But that’s just one property. Say we add in body weight. Now the up and down position of a dot represents blood pressure, the horizontal position represents body weight and you have a 2D plane of dots.
We use 2 parameters to determine the formula for a line (x and y intercept for example) that we tweak just the same as we did the vertical position before.
End result is we have two sections divided by a line that can predict heart attacks.
Add in a third parameter like height. Your dots are now all spread out in a cube, you have 3 parameters to change which determine a plane that slices the cube into two pieces. New combinations of height, blood pressure, and weight will either fall on the heart attack side, or no heart attack side and you can use this to predict again, who is likely to have a heart attack but better.
It gets weird with more dimensions, but you can keep expanding this into n-dimensional cubes separated by hyper planes. When you add tons of different parameters this can end up being surprisingly effective.
And now you have basic machine learning
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