What Is Feature Engineering?
Data sets can be made more predictive with a little help.
“Features” are the properties or characteristics of something you want to predict. Machine learning predictions can often be improved by “engineering”— adjusting features that are already there or adding features that are missing. For instance, date/time fields may appear as nearly unique values that are not predictive. Breaking the single date/time feature into year, month, day, day of the week, and time of day may allow the machine learning model to reveal patterns otherwise hidden.
Automated ML to the Rescue
You can engineer features on your own data sets, but automated feature engineering is now part of advanced machine learning systems. This ability to sense opportunities to improve data set values and do so automatically contributes vastly improved performance without the tedium of doing it manually.
Common kinds of feature engineering include:
- Expansion, as in the date/time example
- Binning – grouping variables with minor variations into fewer values, as in putting all heights from 5’7.51” to 5’8.49” into category 5’8”
- Imputing values – adding, subtracting, or multiplying features that interact
- Removing unused or redundant features
- Text vectoring – deriving commonalities from repeated terms in otherwise unique strings
Ever wonder why Variables of Importance lists don’t exactly match your source data set variables? That’s Squark using automatic feature engineering to improve prediction by reducing or expanding the number of variable columns.
As always, get in touch if you have questions about Feature Engineering or any other Machine Learning topic. We’re happy to help.