FDE Times: ISLP for FDEs — read resampling, regularization and trees before deep learning

FDE Times on 6 November argues that for Forward Deployed Engineers the durable skill in An Introduction to Statistical Learning with Python (ISLP, 2023) is judging whether a model works on the customer's data—not chasing the newest framework. The guide recommends reading the chapter-2 introductory lab and linear regression first, then the middle blocks that matter on site: resampling (cross-validation and the bootstrap), regularization, and tree-based methods, followed by unsupervised learning for early exploration of unlabelled tables.

Its practical lessons match common FDE traps. A score measured on the training set can look like 92% and collapse under 5-fold cross-validation; regularization shows that hundreds of client columns are not free; a shallow decision tree is often the first baseline operators can read and push back on. Cross-validation itself lies when you tune on the same folds you report, or when a column such as "cancellation date" leaks the target—so keep a held-out test set and ask, for every feature, whether it exists at prediction time.

Deep learning, survival analysis and multiple testing chapters can wait until a project needs them. The piece points readers at the official ISLP labs repo and suggests turning a public-dataset notebook—training-set score beside CV score, explained for a non-technical manager—into portfolio evidence for FDE applications.