Partial Dependence Plots: How to Discover Variables Influencing a Model | by Mythili Krishnan

Have you ever wondered how machine learning models are constructed? ‘Explainability of machine learning models’ and ‘machine learning models being a black-box’ is one of the most debated topics in model transparency. Today we will explore this and learn some quick techniques on how to find out which variables are influencing the model results and by how much.

I have generated a synthetic dataset with some variables that depict the statistics of football matches like number of goal scored, number of passes, ball possession %, Number of red or yellow cards etc. Using the dataset we will explore the following models:

1. Decision Tree model
2. Random Forest model

This will be the agenda for today:

1. Train the decision tree model
2. Train the random forest model
3. Explore the influential variables in the models
4. Find the threshold of the influential variables

So without further ado let’s get started..