An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data. W

The correct answer is D. Root mean square error (RMSE). Predicting a continuous numeric value (e.g., a price or count) is a regression problem. Root Mean Square Error (RMSE) is the standard evaluation metric for regression models-it measures the average magnitude of prediction errors, penalizing large errors more heavily due to the sq

ML Model Development

Question

An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data. Which metric should the ML engineer use to evaluate the model’s performance?

Options

AAccuracy
BInferenceLatency
CArea Under the ROC Curve (AUC)
DRoot mean square error (RMSE)

How the community answered

(37 responses)

A
14% (5)
B
3% (1)
C
5% (2)
D
78% (29)

Explanation

Predicting a continuous numeric value (e.g., a price or count) is a regression problem. Root Mean Square Error (RMSE) is the standard evaluation metric for regression models-it measures the average magnitude of prediction errors, penalizing large errors more heavily due to the squaring. A lower RMSE indicates better model accuracy. Accuracy (A) is a classification metric, not applicable for continuous output. InferenceLatency (B) measures speed, not predictive quality. AUC (C) evaluates the discriminative ability of a binary classifier and is irrelevant for regression tasks.

Topics

#Machine Learning Evaluation#Regression#RMSE#SageMaker Canvas

Community Discussion

No community discussion yet for this question.

Full MLA-C01 Practice