This is a tutorial post relating to our python feature selection package,
linselect. The package allows one to easily identify minimal, informative feature subsets within a given data set.
Here, we demonstrate
linselect‘s basic API by exploring the relationship between the daily percentage lifts of 50 tech stocks over one trading year. We will be interested in identifying minimal stock subsets that can be used to predict the lifts of the others.
This is a demonstration walkthrough, with commentary and interpretation throughout. See the package docs folder for docstrings that succinctly detail the API.
- Load the data and examine some stock traces
- FwdSelect, RevSelect; supervised, single target
- FwdSelect, RevSelect; supervised, multiple targets
- FwdSelect, RevSelect; unsupervised
The data and a Jupyter notebook containing the code for this demo are available on our github, here.
linselect package can be found on our github, here.