Computer scienceData scienceMachine learningRegression

Introduction to regression

Theory

Evaluation metrics for regression

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Imagine that you trained a number of regression models and want to compare the quality of their predictions.

Match the most common evaluation metrics for regression models with their definitions.

Match the items from left and right columns
Mean Absolute Error (MAE)
Normalized Root Mean Squared Error (nRMSE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
1n∑i=1n(yi−y^i)2\sqrt{\frac{1}{n}\sum_{i=1}^n(y_i - \hat{y}_i)^2}
1n∑i=1n(yi−y^i)21n∑i=1nyi\frac{\sqrt{\frac{1}{n}\sum_{i=1}^n(y_i - \hat{y}_i)^2}} {\frac{1}{n}\sum_{i=1}^ny_i}
1n∑i=1n(yi−y^i)2\frac{1}{n} \sum_{i=1}^{n}(y_i - \hat{y}_i)^2
1n∑i=1n∣yi−y^i∣\frac{1}{n}\sum_{i=1}^n|y_i - \hat{y}_i|
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