MLA-C01 · Question #190
An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data…
The correct answer is A. Remove the outliers and perform a log transformation on the Square Meters variable. Removing extreme outliers prevents rare, atypical properties from disproportionately influencing the model, and applying a log transformation to square meters reduces skew in the feature distribution. This helps the model learn patterns that better represent typical houses and…
Question
An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment. The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment. Which solution will meet these requirements?
Options
- ARemove the outliers and perform a log transformation on the Square Meters variable.
- BKeep the outliers and perform normalization on the Square Meters variable.
- CRemove the outliers and perform one-hot encoding on the Square Meters variable.
- DKeep the outliers and perform one-hot encoding on the Square Meters variable.
How the community answered
(58 responses)- A78% (45)
- B3% (2)
- C12% (7)
- D7% (4)
Explanation
Removing extreme outliers prevents rare, atypical properties from disproportionately influencing the model, and applying a log transformation to square meters reduces skew in the feature distribution. This helps the model learn patterns that better represent typical houses and apartments, resulting in more accurate predictions.
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