Local explanations target specific predictions from the machine learning model. The goal is to understand why the model made a particular prediction.
There are multiple different forms of local explanations, such as feature attribution explanations and examplar-based explanations. ADS currently supports local feature attribution explanations. They help to identify the most important features leading towards a given prediction.
While a given feature might be important for the model in general, the values in a particular sample may cause certain features to have a larger impact on the model’s predictions than others. Furthermore, given the feature values in a specific sample, local explanations can also estimate the contribution that each feature had towards or against a target prediction. For example, does the current value of the feature have a positive or negative effect on the prediction probability of the target class? Does the feature increase or decrease the predicted regression target value?