Prediction Biases and Z-Scores

Prediction Biases

Prediction bias is a measure of how far apart the average of our predictions is from the average of our observations. That is:

$prediction\ bias = average\ of\ predictions - average\ of\ labels\ in\ data\ set$

In a perfect world, there is no difference between those two values.

Normalizing Values & Z-Scores

When working with multiple features, we want to ensure values are in about the same range. Eg: if one feature has values between 1-1,000,000, and another has values between 0.5-3.5, the model will be difficult to train. So, we need to normalize the values we have.

Introducing Z-Scores

One handy way of doing that is by wowkring with Z-Scores: that is, the number of standard deviations from the mean for a value. Eg:

Suppose the mean is 20, and the standard deviation is 3. A value of 25 would have a Z score of $(25 - 20) / 3 = +1.67$