Squeezing the most info out of your data
... or how to use Feature Selection and Feature Engineering
Between EDA and data cleaning (depression) and algorithm training
(fancy stuff) there's an uncharted territory of feature selection and engineering
First, a bit of reason
Why feature engineering?
"Because"
Igor Dodon
Categorical variables encoding
Encoding Schemes
- One Hot encoding
- Frequency encoding
- Mean Target encoding
- Weigh of Evidence encoding
Continuous variables discretisation
Transformations
Transformation Schemes
- Multiplication
- Logarithms
- Power
- Trigonometric
Combinations
First, a bit of reason
Part 2
Why feature selection?
"Through feature engineering have gone you once, lots of data have you."
Yoda
Correlations
Mutual information
Model importance based
A note on Boruta
A note on dimensionality reduction
In case Facebook was more interesting - TL;DR:
- Not all features are necessary
- Some might even be detrimental
- If you have domain knowledge - use it!
- A pretty simple strategy - make as many feature as possible, then keep only the best
- Categorical variables must be treated specially
Thank you
Questions?
Squeezing the most info out of your data
By Alexandru Burlacu
