Methods for machine learning interpretability can be classified according to various criteria.
Intrinsic or post hoc? This criteria distinguishes whether interpretability is achieved by restricting the complexity of the machine learning model (intrinsic) or by applying methods that analyze the model after training (post hoc). Intrinsic interpretability refers to machine learning models that are considered interpretable due to their simple structure, such as short decision trees or sparse linear models. Post hoc interpretability refers to the application of interpretation methods after model training. Permutation feature importance is, for example, a post hoc interpretation method. Post hoc methods can also be applied to intrinsically interpretable models. For example, permutation feature importance can be computed for decision trees. The organization of the chapters in this book is determined by the distinction between intrinsically interpretable models and post hoc (and model-agnostic) interpretation methods.
Result of the interpretation method The various interpretation methods can be roughly differentiated according to their results.
- Feature summary statistic: Many interpretation methods provide summary statistics for each feature. Some methods return a single number per feature, such as feature importance, or a more complex result, such as the pairwise feature interaction strengths, which consist of a number for each feature pair.
- Feature summary visualization: Most of the feature summary statistics can also be visualized. Some feature summaries are actually only meaningful if they are visualized and a table would be a wrong choice. The partial dependence of a feature is such a case. Partial dependence plots are curves that show a feature and the average predicted outcome. The best way to present partial dependences is to actually draw the curve instead of printing the coordinates.
- Model internals (e.g. learned weights): The interpretation of intrinsically interpretable models falls into this category. Examples are the weights in linear models or the learned tree structure (the features and thresholds used for the splits) of decision trees. The lines are blurred between model internals and feature summary statistic in, for example, linear models, because the weights are both model internals and summary statistics for the features at the same time. Another method that outputs model internals is the visualization of feature detectors learned in convolutional neural networks. Interpretability methods that output model internals are by definition model-specific (see next criterion).
- Data point: This category includes all methods that return data points (already existent or newly created) to make a model interpretable. One method is called counterfactual explanations. To explain the prediction of a data instance, the method finds a similar data point by changing some of the features for which the predicted outcome changes in a relevant way (e.g. a flip in the predicted class). Another example is the identification of prototypes of predicted classes. To be useful, interpretation methods that output new data points require that the data points themselves can be interpreted. This works well for images and texts, but is less useful for tabular data with hundreds of features.
- Intrinsically interpretable model: One solution to interpreting black box models is to approximate them (either globally or locally) with an interpretable model. The interpretable model itself is interpreted by looking at internal model parameters or feature summary statistics.
Model-specific or model-agnostic? Model-specific interpretation tools are limited to specific model classes. The interpretation of regression weights in a linear model is a model-specific interpretation, since – by definition – the interpretation of intrinsically interpretable models is always model-specific. Tools that only work for the interpretation of e.g. neural networks are model-specific. Model-agnostic tools can be used on any machine learning model and are applied after the model has been trained (post hoc). These agnostic methods usually work by analyzing feature input and output pairs. By definition, these methods cannot have access to model internals such as weights or structural information.
Local or global? Does the interpretation method explain an individual prediction or the entire model behavior? Or is the scope somewhere in between? Read more about the scope criterion in the next section.