You predict house prices. Which metric fits?
Why D is the answer
RMSE
RMSE measures error for numeric predictions. The others are for classification.
In detail
- House price is a number, so use a regression metric like RMSE.
- RMSE penalizes large errors more.
Use case
An illustrative scenario, not a real incident.
A team predicts prices, and RMSE reports the typical error in currency.
See it in different ways
flowchart TD
A{"Which error hurts most?"} -->|"False alarms"| B["Precision"]
A -->|"Missed cases"| C["Recall"]
A -->|"Both, with imbalanced classes"| D["F1 score"]
A -->|"Numeric predictions"| E["RMSE"]mindmap
root((Metrics))
Classification
Recall: catch every case
Precision: avoid false alarms
Regression
RMSE
Text
ROUGEflowchart TD
Q{"What does the model predict?"}
Q -->|"A number"| A0["RMSE"]
Q -->|"A class"| A1["Precision, recall, F1"]Key point: RMSE fits numeric predictions.
Why the other options are wrong
- A. Precision
- Precision is for classification.
- B. Recall
- Recall is for classification.
- C. F1 score
- F1 is for classification.