Classification Model Explanation

Explainable models, also known as interpretable models, are machine learning models designed to be transparent and understandable to humans. The goal of explainable models is to help users understand why a particular decision was made by the model and build trust in the decision-making process.

There are several approaches to building explainable models. One approach is to use linear models, which are inherently interpretable. Another approach is to use more complex models like gradient boosting trees but augment them with techniques like feature importance analysis or sensitivity analysis, which help identify the key features driving the model’s decisions.

In this lesson, let’s explore the classifier model that we built with Lending Club loans data, using the explainable modules available in the H2O package, and gain more insights from it. We’ll have a detailed discussion on the following interpretability techniques:

• Variable importance

• Individual conditional expectation (ICE) plot

• Partial dependence plots

Understanding variable importance

Variable importance is a statistical method to assess the importance of each feature or variable in a dataset for predicting the outcome of interest. It helps define the relationship between variables and prediction, identify the most significant factors, and improve model accuracy. Various techniques can be used to calculate variable importance by analyzing the relative importance of each variable.

Feature importance is typically evaluated by measuring the impact of including or excluding a particular feature on the overall performance of the model. By prioritizing the most important variables, we can gain deeper insights into the patterns and relationships in the data and build better models.

The _varimp_plot() method in H2O allows us to visualize the relative importance of each feature in the model, making it easier to identify the most important variables: