Coursera Machine Learning Study notes (ix)

-Feature Scaling

When we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.

Take the housing price forecast problem as an example, assuming that the two characteristics we use, namely the size of the house and the number of rooms, the size value range is 0-2000 square feet, and the value of the room number is 0-5, which causes the gradient descent algorithm to require a very many iterations to converge:

To do this, we need to shrink the multidimensional features so that all the features are scaled as far as possible before -1~1. Therefore, our solution is to make:

Where (the average of a feature in a training sample) is the mean, (the difference between the maximum and minimum of a feature in a training sample) is the standard deviation.



Coursera Machine Learning Study notes (ix)