Mastering Model Explainability in Python

For data scientists, a key part of interpreting machine learning models is understanding which factors impact predictions. In order to effectively use machine learning in their decision-making processes, companies need to know which factors are most important. For example, if a company wants to predict the likelihood of customer churn, they might also want to know what exactly drives a customer to leave a company. In this example, the model might indicate that customers who purchase products that rarely go on sale are much more likely to stop purchasing. Armed with this knowledge, a company can make smarter pricing decisions in the future.