MOST COMMON MISTAKES IN MACHINE LEARNING AND HOW TO AVOID THEM
Welcome
- About the Author
Dedication
Protecting the Neural Tree
Preface
Introduction
- Terminology
1 Not understanding the data
2 Reporting train performance
3 Not setting a seed value
4 Including irrelevant features
5 Ignoring differences in scales
6 Using the test set for fine tunning
7 Only reporting accuracy
8 Not comparing against a baseline
9 Not accounting for variance
10 Injecting data into the test set
11 Not shuffling the training data
12 Not saving the results
13 Not parallelizing
14 Encoding categories as integers
15 Forget data changes over time
16 Ignoring inter-user variance
17 Wasting unlabeled data
Appendix
A Setup Your Environment
B Datasets
Citing this Book
References

MOST COMMON MISTAKES IN MACHINE LEARNING AND HOW TO AVOID THEM

Dedication

To My Family, who have put up with me despite my continuous mistakes.