Python Machine Learning By Example

Best practice 9 - decide on whether or not to scale features

Recall that in Chapter 9, Stock Prices Prediction with Regression Algorithms, SGD-based linear regression and SVR models require features to be standardized by removing the mean and rescaling to unit variance. So when is feature scaling needed and when is it not?

In general, naive Bayes and tree-based algorithms are not sensitive to features at different scales, as they look at each feature independently. Logistic or linear regression normally is not affected by the scales of input features, with one exception, when the weights are optimized with stochastic gradient descent.

In most cases, an algorithm that involves any form of distance (separation in spaces) of samples in learning ...