In this chapter, we've learned a large number of ML algorithms, each with its own pros and cons. In this section, we'll look into some common problems and ways to resolve them.
Improving your model – tips and tricks
Feature scaling to resolve uneven data scale
The data that's collected normally doesn't have the same scale; for example, one feature may be varying in the range 10–100 and another one may be only distributed in range 2–5. This uneven data scale can have an adverse effect on learning. To resolve this, we use the method of feature scaling (normalization). The choice of normalization has been found to drastically affect the performance of certain algorithms. Two...
