“Introduction To Statistical Learning With Applications In R” (ISLR) has become a popular textbook for those entering the field of statistical learning and data science. As an educator preparing to teach a course based on the second edition, a thorough review is essential. Initial impressions of the updated edition are positive, particularly noting the inclusion of discussions on contemporary topics like double descent in the deep learning chapter, reflecting the evolving landscape of machine learning.
However, reflecting on past experiences with the first edition, particularly Chapter 3 focusing on linear regression, reveals some pedagogical considerations. While ISLR serves as an excellent review for individuals already familiar with regression concepts, its suitability for total novices is questionable. The presentation, while comprehensive, might be too dense and fast-paced for those encountering these ideas for the first time. Novice learners often require more gradual explanations, additional illustrative examples, and a deeper exploration of the underlying rationale behind different techniques to truly grasp the material. For those with prior regression coursework, though, this chapter provides a solid and efficient refresher.
A further critical point arises in the book’s brief treatment of hypothesis testing. Although prediction is the primary focus of ISLR, the inclusion of hypothesis testing, while relevant, could be pedagogically improved. The conventional framing of statistical hypotheses around parameters (e.g., “is the regression slope β1 = 0?”) can be misleading for students. The essence of hypothesis testing is arguably less about the parameters themselves and more about the dataset – its design and size – and the precision of our measurements. Failing to reject the null hypothesis should not be interpreted as concluding that an effect is definitively zero. Instead, it often signifies a lack of sufficient data to estimate the effect precisely.
This nuance is crucial because it opens up several important decision pathways: collecting more data to improve estimation, removing a noisy variable to mitigate overfitting even if it might have a real effect, or retaining it to prevent underfitting despite estimation uncertainty. Framing hypothesis testing solely around parameter values risks obscuring these vital connections to core concepts like the bias-variance tradeoff and the critical decisions surrounding model complexity. A more nuanced discussion would significantly enhance students’ understanding and bridge the gap between hypothesis testing and broader model building strategies in statistical learning.
In conclusion, “Introduction to Statistical Learning with Applications in R” is a valuable resource, particularly for those with some statistical background or as a review text. The inclusion of modern topics in the second edition is commendable. However, educators should be mindful of the potentially dense presentation for absolute beginners, especially in foundational chapters like linear regression, and supplement the discussion on hypothesis testing to emphasize its practical implications in model selection and data interpretation.
