Outputs you create
answer- The requested result, with the values, labels and row order described by this task.
brief | artifact | has_prediction_target |
|---|---|---|
| repair time | hours prediction | true |
| nested accounts | merge hierarchy | false |
| sensor storage | component scores | false |
- Select the briefs requiring historical prediction outcomes into
answer. - Keep the requested
artifactcolumn to distinguish numeric forecasts from grouping and compression.
answerHints
Think: Repair forecasting returns numeric predictions and errors in hours and needs historical durations. Nested discovery returns a linkage/hierarchy with qualified group profiles; compression returns PCA coordinates with retention evidence. The latter two do not need a prediction target. Their geometrical and retention summaries are not supervised accuracy scores and cannot be ranked on a common accuracy leaderboard.
Tools: Model-choice retrieval: use the supplied brief, artifact, has_prediction_target columns and the pandas or model operations introduced in this lesson.
Approach: Compute answer from the supplied evidence. Preserve the requested labels and row order, then inspect the values before checking.
Explained solution
answer = df.loc[df['has_prediction_target']].copy()
print(answer)
Repair forecasting returns numeric predictions and errors in hours and needs historical durations. Nested discovery returns a linkage/hierarchy with qualified group profiles; compression returns PCA coordinates with retention evidence. The latter two do not need a prediction target. Their geometrical and retention summaries are not supervised accuracy scores and cannot be ranked on a common accuracy leaderboard.