A data scientist needs to predict the exact sale price of a house given its features such as square footage, number of bedrooms, and location. Which cuML algorithm family and target type correctly describe this task?
- AClassification, because the model assigns each house to a price category
- BClustering, because grouping similar houses reveals natural price segments without needing labelled training data
- CRegression, because the target variable is a continuous numeric value and the model learns from labelled training examples Correct
- DDimensionality reduction, because feature engineering must precede any numeric prediction task
Why A is wrong: Classification predicts a discrete label from a finite set of categories, not a continuous numeric value such as a sale price expressed in dollars
Why B is wrong: Clustering is unsupervised and produces group assignments rather than a numeric prediction for each individual observation; labelled sale prices already exist in the training set
Why C is correct: Regression is the supervised learning family for continuous targets. Sale price is a continuous numeric output, and cuML provides GPU-accelerated estimators such as linear regression and random forest regressor that fit this exact task
Why D is wrong: Dimensionality reduction is a data preprocessing technique, not a predictive modelling family; it does not produce per-sample numeric predictions and does not replace the choice of regression