Evaluating an Apartment Price Prediction Model with Mean Absolute Error Instead of Classification Metrics
An ML engineer needs to use an ML model to predict the price of apartments in a specific location. Which metric should the ML engineer use to evaluate the model's performance?
Community Votes
100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
Mean absolute error measures the average magnitude of prediction errors in the same units as the target, which is the meaningful measure for a continuous price prediction. Accuracy, AUC, and F1 score are all defined over class predictions and are not applicable to a regression output.
An ML engineer is predicting apartment prices, which is a continuous numeric target rather than a discrete class label. The choice of evaluation metric has to match the problem type, and the options split cleanly into one regression metric and three classification metrics.
Reaching for accuracy, AUC, or F1 score because they are the most familiar ML metrics. Those three are classification metrics and are undefined for a continuous price output, so no matter how well they score, they do not evaluate the model.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Predicting the price of an apartment is a regression problem, because the target is a continuous numeric value rather than a class label. Mean absolute error is the regression metric that averages the absolute difference between predicted and actual prices, expressed in the same currency units as the target, so it directly quantifies how far off the price predictions are. That makes it the only metric in the list that evaluates this model's performance. The vote was unanimous at 100 for D, and GiorgioGss identified the core distinction in one line, noting that D is the only regression metric while the other three are for classification. ninomfr64 and lunachi4 both restated that the apartment price prediction is a regression task, so MAE is the correct choice.Why the Other Options Are Wrong
Accuracy (A) measures the proportion of correct class predictions and is undefined when the output is a continuous price, so it cannot evaluate this model. Area under the ROC curve (B) is built from true positive and false positive rates across decision thresholds and applies to binary or multiclass classification, not to a numeric price prediction. F1 score (C) is the harmonic mean of precision and recall over predicted classes, which again requires discrete class labels and has no meaning for a continuous regression output. All three of these measure different aspects of classification quality and are inapplicable here regardless of how the model is tuned.Community Comment Notes
The community was unanimous at 100 for D and every substance comment agreed. GiorgioGss gave the shortest correct explanation, that the other three options are classification metrics and only D is for regression. ninomfr64 and lunachi4 both identified the problem type as regression as the deciding step, which is the same reasoning chain the question intends: determine the problem type first, then pick the metric that matches it.Official Reference
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