Which two metrics can you use? Each correct answer presents a complete solution?

You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the regression model.

Which two metrics can you use? Each correct answer presents a complete solution? NOTE: Each correct selection is worth one point.
A . a Root Mean Square Error value that is low
B . an R-Squared value close to 0
C . an F1 score that is low
D . an R-Squared value close to 1
E . an F1 score that is high
F . a Root Mean Square Error value that is high

View Answer

Answer: AD

Explanation:

RMSE and R2 are both metrics for regression models.

A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

Incorrect Answers:

C, E: F-score is used for classification models, not for regression models.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model