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Missing values
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Classroom Contents
Garbage Data In, Garbage Models Out - How to Select the Right Data for Robust Machine Learning
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- 1 Intro
- 2 Learning machine learning
- 3 Basic model types
- 4 The danger of assumptions
- 5 What do our features mean?
- 6 How do we see how good a model is?
- 7 Reserve data for testing
- 8 Missing values
- 9 Detecting outliers
- 10 Adjusting for class imbalance
- 11 Curse of dimensionality
- 12 Feature engineering
- 13 Creating generalisable models
- 14 Selection bias
- 15 Survivorship bias
- 16 How does your model make predictions?
- 17 Are you using the right metrics?
- 18 Is newer and shiner better?
