Machine learning is the set of computer methods to learn and improve predictions and decisions based on the data. The degree that produces the model’s results is comprehensible and consistently predicted by humans. An increase in interpretability of the machine learning model provides decisions that are easily understandable and troubleshoot. Interpretable machine learning is capable to builds trustworthy human-understandable models. Interpretable models achieve fairness, robustness, privacy, causality, and trust for machine learning.

The significance of interpretability in machine learning is better in avoiding sudden consequences. The scope of the interpretable model is to be global or local interpretability. Advantages of interpretable machine learning satisfy human curiosity, discover meaning, social acceptance, scientific findings, safety, and acquire new knowledge.

The techniques in interpretable machine learning are categorized as either model-specific or model acoustic. Some of the algorithms of interpretable models are linear regression, logistic regression, decision tree, naive Bayes, k-nearest neighbor, rule fit, generalized linear, and additive models. Highly employed application fields of interpretable machine learning are medicine and finance.