MLS-C01 · Question #380
MLS-C01 Question #380: Real Exam Question with Answer & Explanation
The correct answer is B: Mean squared error (MSE). For a model predicting package delivery time, which is a regression problem, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are the appropriate performance evaluation metrics.
Question
A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Choose two.)
Options
- AInferenceLatency
- BMean squared error (MSE)
- CRoot mean squared error (RMSE)
- DPrecision
- EAccuracy
Explanation
For a model predicting package delivery time, which is a regression problem, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are the appropriate performance evaluation metrics.
Common mistakes.
- A. InferenceLatency measures the time taken for a model to make a prediction, which is an operational metric, not a measure of the model's predictive performance or accuracy.
- D. Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions, making it unsuitable for regression problems where continuous values are predicted.
- E. Accuracy is a classification metric that measures the overall proportion of correct predictions, which is not applicable to regression models that predict continuous numerical outcomes.
Concept tested. Regression model evaluation metrics
Reference. https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-model-metrics.html
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