Table of Contents
Preface
1
An Overview of Regularization
Technical requirements
Introducing regularization
Examples of models that did not pass the deployment test
Intuition about regularization
Key concepts of regularization
Bias and variance
Underfitting and overfitting
Regularization – from overfitting to underfitting
Unavoidable bias
Diagnosing bias and variance
Regularization – a multi-dimensional problem
Summary
2
Machine Learning Refresher
Technical requirements
Loading data
Getting ready
How to do it…
There’s more…
See also
Splitting data
Getting ready
How to do it…
See also
Preparing quantitative data
Getting ready
How to do it…
There’s more…
See also
Preparing qualitative data
Getting ready
How to do it…
There’s more…
See also
Training ...
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