August 2018
Intermediate to advanced
522 pages
12h 45m
English
In this section, we are going to analyze the most common regularization methods and how they can impact the performance of a linear regressor. In real-life scenarios, it's very common to work with dirty datasets, containing outliers, inter-dependent features, and different sensitivity to noise. These methods can help the data scientist mitigate the problems, yielding more effective and accurate solutions.
Read now
Unlock full access