# Why do we need to normalize the characteristics of numerical types? Why does the tree algorithm not need to normalize?

Time：2019-10-21

For linear models, when the eigenvalues are very different, for example, LR, I have two characteristics, one is (0,1) and the other is (010000). When using gradient descent, the loss contour is elliptical, which requires multiple iterations to reach the best.

However, if the normalization is carried out, the contour is circular, which makes SGD iterate to the origin, resulting in less iterations.

So it is because the gradient descent algorithm needs to be normalized, which speeds up the speed of solving the optimal solution of gradient descent.

When looking for the best advantage of tree model (regression tree), it is done by looking for the optimal split point, which can be seen from the implementation of decision tree ID3 algorithm python. Because there’s no point in deriving, there’s no need for normalization.

## The way of nonlinear optimization

Mathematical knowledge 1、 Nonlinear functionLinear function is another name of a function of first degree, then nonlinear function means that the function image is not a function of a straight line.Nonlinear functions include exponential function, power function, logarithmic function, polynomial function and so on. 2、 Taylor expansion1. Taylor formula:Taylor’s formula is to add a_ The […]