Machine Learning Algorithms

Normalization

Normalizing a numeric dataset is one of the most important steps, particularly when different features have different scales. In Chapter 3, Feature Selection and Feature Engineering, we discussed several methods that can be employed to solve this problem. Very often, it's enough to use a StandardScaler to whiten the data, but sometimes it's better to consider the impact of noisy features on the global trend and use a RobustScaler to filter them out without the risk of conditioning the remaining features. The reader can easily verify the different performances of the same classifier (in particular, SVMs and neural networks) when working with normalized and unnormalized datasets. As we're going to see in the next section, it's ...