You are a data scientist creating a linear regression model. You need to determine how closely the data fits the regression line. Which metric should you review?

The correct answer is B. Coefficient of determination. 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…

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Question

You are a data scientist creating a linear regression model. You need to determine how closely the data fits the regression line. Which metric should you review?

Options

ARoot Mean Square Error
BCoefficient of determination
CRecall
DPrecision
EMean absolute error

How the community answered

(25 responses)

A
8% (2)
B
88% (22)
D
4% (1)

Explanation

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: 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. C: Recall is the fraction of all correct results returned by the model. D: Precision is the proportion of true results over all positive results. E: Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-

Topics

#Regression model evaluation#Coefficient of determination#R-squared#Model performance metrics

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