Understand the idea
Reports should name the task, population, evaluation design, reference, selected approach and limitations. Discovery reports describe profiles and assumptions; PCA reports retention and representation rather than prediction accuracy.
Python skill: Organises the supplied comparison evidence into a report table.
Meet the syntax
pd.DataFrame({'model': names, 'cv_rmse': errors, 'fold_sd': variation})pd.DataFrame- Organises the supplied comparison evidence into a report table.
'cv_rmse'- Names the training-validation error summary; the supplied values are illustrative, not newly fitted results.
'fold_sd'- Names variation across folds, not an uncertainty guarantee or an independent final-test result.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
answer=pd.DataFrame({'model':['linear','tree'],'cv_rmse':[4.2,4.3],'fold_sd':[.6,.8]})
This practice: Read and run the Python. Next: Change · Explain a result responsibly.
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
Build answer as an evidence table with columns model, cv_rmse and fold_sd. The linear model has CV RMSE 4.2 and fold standard deviation 0.6; the tree has 4.3 and 0.8.
Hint 1 — Think
A comparison table should keep each model beside its matching score and fold variation.
Hint 2 — Tools
pd.DataFrame with model, cv_rmse and fold_sd columns.
Hint 3 — Approach
Enter the two stated model results as aligned rows in the named columns.
Explained solution
answer=pd.DataFrame({'model':['linear','tree'],'cv_rmse':[4.2,4.3],'fold_sd':[.6,.8]})
The table keeps training-fold error and its variation together, which helps qualify the small difference between the model means.
Helpful prior knowledge: Compare candidates on common evidence 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.