Under- and Overfitting: Training vs Validation Error by Model Complexity
This lesson is part of the course
In this lesson
You compare training and validation error for trees of depth 1 to 8, compute the gap, label every depth as underfitting, good fit or overfitting, find the sweet spot and draw both curves in one line chart.
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Lessons in this course
- Three Piles of Data: Train, Validation & Test Splits from a Shuffle Number Locked
- k-Fold Cross-Validation: Assigning Folds with MOD and Stratifying by Class Locked
- A Toy Model per Fold: k-Nearest-Neighbour Predictions for a Held-Out Day Locked
- Fold Scores: Mean Absolute Error per Fold, the CV Estimate and Its Spread Locked
- Grid Search: A Setting × Fold Table, the Best k and the One-Standard-Error Rule Locked
- Under- and Overfitting: Training vs Validation Error by Model Complexity Locked
- Data Leakage: Scaling Before Splitting, Duplicates Across Folds and Rolling-Origin Checks Locked
- The Final Report: Choose by CV, Retrain on All Pool Days and Score the Test Set Once Locked