Model-agnostic interpretation by visualization of feature perturbations
This addresses the need for interpretability in machine learning to avoid bias, but it is incremental as it builds on existing perturbation-based methods with a specific optimization technique.
The paper tackles the problem of interpreting diverse machine learning models by proposing a model-agnostic approach using visualization of feature perturbations induced by the PSO algorithm, validated on public datasets to enhance interpretation with stable results compared to state-of-the-art methods.
Interpretation of machine learning models has become one of the most important research topics due to the necessity of maintaining control and avoiding bias in these algorithms. Since many machine learning algorithms are published every day, there is a need for novel model-agnostic interpretation approaches that could be used to interpret a great variety of algorithms. Thus, one advantageous way to interpret machine learning models is to feed different input data to understand the changes in the prediction. Using such an approach, practitioners can define relations among data patterns and a model's decision. This work proposes a model-agnostic interpretation approach that uses visualization of feature perturbations induced by the PSO algorithm. We validate our approach on publicly available datasets, showing the capability to enhance the interpretation of different classifiers while yielding very stable results compared with state-of-the-art algorithms.