Interpretability v/s Flexibility for different statistical learning methods

 

(Source: An Intoduction to Statistical Learning with Applications in R written by James G., Witten D., Hastie T. and Tibshirani R. and published by Springer Text in Statistics. This is a part of my machine learning specialization course from CU Boulder.)

    I learned from this topic that we can choose a method from multiple statistical learning topics based on our goal. This book shares an example saying that if we are interested in predicting the accurate values in the stock market and not much concerned about inference (how each predictor variable is associated with the output), we can use high-flexibility approaches like deep learning. However, if we are concerned about the inference, we can use high-interpretability models like Lasso or Least-squares. The book also states that sometimes the high-interpretability models give more accurate results.