Understanding ML explainability

There are two main concepts when it comes to explaining the behaviors of an ML model:

• Global explainability: This is the overall behavior of a model across all data points used for model training and/or prediction. This helps to understand collectively how different input features affect the outcome of model predictions. For example, after training an ML model for credit scoring, it is determined that income is the most important feature in predicting high credit scores across data points for all loan applicants.
• Local explainability: This is the behavior of a model for a single data point (instance), and which features had the most influence on the prediction for a single data point. For example, when you try to explain which features influenced the decision the most for a single loan applicant, it might turn out that education was the most important feature, even though income was the most important feature at the global level.
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