- Block 8 — Training the model
- Objective
- separate three notions that the single English word "error"
routinely collapses into one — the discrepancy on a single example, the
differentiable objective the optimizer actually minimizes over the training
set, and the non-differentiable business-facing quantity used to judge and
select the model. Give each a rigorous definition, establish what statistical
functional each loss estimates, explain why the loss and the metric are
usually different and why that divergence is structural rather than a defect,
and operate the scikit-learn
scoring interface without falling into its
sign convention traps. - Estimated duration
- 55 minutes
- Prerequisites
- chapters 008 (algorithm and model), 009 (parameters and
hyperparameters), 026 (train/validation/test split) and 029 (fit, predict,
predict_proba)
- Associated quizzes
030.1-quiz-the-three-objects.md to
030.8-quiz-scoring-interface.md