Understand the idea
Comparisons need the same population, outcome definition, split and folds when the aim is a paired predictive comparison. Nominate before the final test. Discovery and PCA use different evidence and must not be ranked by supervised accuracy.
Python skill: Compare aligned fold-score columns before summarising candidate performance.
Meet the syntax
fold_errors['tree'] - fold_errors['line']fold_errors['tree'] - fold_errors['line']- Subtracts errors on matching folds. A negative value favours the tree on that fold.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
fold_errors = pd.DataFrame({'line': [4.0, 5.0, 4.5, 5.5, 4.0], 'tree': [3.5, 4.8, 5.0, 4.9, 3.8]})
answer = fold_errors['tree'] - fold_errors['line']
This practice: Read and run the Python. Next: Change · Compare candidates on common evidence.
Given data · LINE24
24 observations. Deterministic teaching observations; values illustrate the concept rather than a real population claim. The dataframe df is supplied afresh for each Run.
| distance | duration |
|---|---|
| 1 | 5.30501 |
| 1.47826 | 11.2361 |
| 1.95652 | 11.8488 |
| 2.43478 | 14.809 |
| 2.91304 | 10.7589 |
| 3.3913 | 19.4972 |
| 3.86957 | 17.89 |
| 4.34783 | 15.531 |
Column meanings and units
Column names describe the supplied features and target. Keep the stated units and row identities when making comparisons.
Synthetic data are deliberately small and reproducible. Their patterns illustrate an idea; they are not evidence about a real population.
| Column | Stored type |
|---|---|
| distance | float64 |
| duration | float64 |
Your task · Follow
Across five matching folds, line RMSE is [4.0, 5.0, 4.5, 5.5, 4.0] and tree RMSE is [3.5, 4.8, 5.0, 4.9, 3.8]. Store tree minus line RMSE for each fold in answer.
Hint 1 — Think
Compare aligned fold-score columns before summarising candidate performance.
Hint 2 — Tools
Use fold_errors['tree'] - fold_errors['line']. Read the visible syntax meanings before editing.
Hint 3 — Approach
Across five matching folds, line RMSE is [4.0, 5.0, 4.5, 5.5, 4.0] and tree RMSE is [3.5, 4.8, 5.0, 4.9, 3.8]. Store tree minus line RMSE for each fold in answer. Keep the supplied row order and inspect the named output after running.
Explained solution
fold_errors = pd.DataFrame({'line': [4.0, 5.0, 4.5, 5.5, 4.0], 'tree': [3.5, 4.8, 5.0, 4.9, 3.8]})
answer = fold_errors['tree'] - fold_errors['line']
The tree has smaller RMSE in four of these illustrative folds but larger error in one. Pairing the evidence preserves the common evaluation population. The mean difference alone is not a universal ranking or proof of statistical significance.
Helpful prior knowledge: Choose the task before the family These links are guidance, not locks.
Sources and API context
Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.