Machine learning models are used everywhere these days and it has become very easy to interpret some of these machine learning models. We will be looking at a few specific tools which help us interpret these models and better understand the results these models provide. Here are those libraries
There are a few files here which talk about different things.
- preprocessing.ipynb talks about preprocessing the data and performing steps needed to build a machine learning model. Not the most important for now
- Decision Trees Interpretation.ipynb talks about building decision trees, a machine learning model and how to interpret them
- Counterfactuals.ipynb talks about generating what-if scenarios based on the current data and identifying what it would take for a point to get classified in the other class