DP-100 Exam Dumps | You are analyzing a dataset containing historical data from a local taxi company. You are developing

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Question 62/162

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

Correct Answer: A,D

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

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