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Machine learning Models
This book discusses two Explainable models
- 1.Linear regression
- 2.Decision Tree
Every machine learning engineer or data scientist learns these two models at the start of their career and every coursework will contain these two machine learning models as the fundamental models. The rationale behind this is they are easy to understand and learn from. These models make sense and people can easily connect them with their daily life.
There are cases where simple Explainable Machine Learning Models fail to give self-explanation. For example, when there is multi-collinearity or when there is an interaction effect among the features, the linear models fail to explain themselves and might need the help of some other methods to make them Explainable. We will explain how Linear models and Decision Trees are explainable and move on to other techniques to achieve interpretability in the later parts of this book.