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.
What the syntax does
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.
Outputs you create
answer- The requested result, with the values, labels and row order described by this task.
Evidence to use
- Illustrative reporting evidence, not scores fitted here or results from the supplied teaching dataframe: predict completion time in hours for repair jobs at one workshop, using measurements available at intake.
- Of 100 historical jobs, 80 are development rows and 20 are reserved for a later final test; no final-test result is available.
- All three workflows use the same five shuffled development folds, seed 42, with preparation fitted within each training fold.
- The dummy predicts that training fold's mean completion time.
- Mean validation RMSE / fold standard deviation, both in hours: dummy 4.4 / 0.7; linear 4.2 / 0.6; tree 4.3 / 0.8.
- Fold standard deviations describe variation, not confidence intervals; paired fold scores are not supplied.
- Build
answerfrom the supplied illustrative reporting evidence, with columnsmodel,cv_rmseandfold_sd, in dummy, linear, tree row order. - Include the training-mean reference as well as both candidates.
- All errors and fold standard deviations are in hours
- Do not fit models or claim a final-test result.
answerHints
Think: A comparison table should keep the dummy reference beside both candidates and their fold variation.
Tools: pd.DataFrame with model, cv_rmse and fold_sd columns; all values are in hours.
Approach: Enter dummy, linear and tree as three aligned rows using the supplied illustrative results.
Explained solution
answer=pd.DataFrame({'model':['dummy','linear','tree'],'cv_rmse':[4.4,4.2,4.3],'fold_sd':[.7,.6,.8]})
The table keeps the dummy reference and both candidates on the same five development folds. Fold standard deviations describe variation rather than confidence intervals; the small mean differences alone do not establish reliable improvement.