Learn the key metrics a data scientist should utilize when evaluating the performance of a machine learning model that predicts package delivery times for an ecommerce company. Discover why mean squared error (MSE) and root mean squared error (RMSE) are the optimal choices.
Table of Contents
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.)
A. InferenceLatency
B. Mean squared error (MSE)
C. Root mean squared error (RMSE)
D. Precision
E. Accuracy
Answer
The two metrics the data scientist should use to evaluate the performance of the delivery time prediction model are:
B. Mean squared error (MSE)
C. Root mean squared error (RMSE)
Explanation
MSE and RMSE are the most appropriate metrics for this scenario because the model’s objective is to predict a continuous numerical value (the number of minutes for package delivery). MSE calculates the average squared difference between the predicted and actual values, while RMSE is the square root of MSE. Both metrics provide a clear indication of how well the model’s predictions align with the actual delivery times.
InferenceLatency (A) is not relevant for evaluating the model’s predictive performance; instead, it measures the time taken for the model to generate predictions. Precision (D) and Accuracy (E) are metrics used for classification problems, where the goal is to predict discrete categories or labels. Since this model predicts a continuous value, precision and accuracy are not applicable.
By using MSE and RMSE, the data scientist can effectively assess the model’s performance and make necessary adjustments to improve its predictive capabilities. These metrics will help ensure that the ecommerce company’s delivery time predictions are as accurate as possible.
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