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 metric

Question

Options

• Aa Root Mean Square Error value that is low
• Ban R-Squared value close to 0
• Can F1 score that is low
• Dan R-Squared value close to 1
• Ean F1 score that is high
• Fa Root Mean Square Error value that is high

• A
  90% (26)
• C
  3% (1)
• E
  7% (2)

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

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