Data Science
Performance Evaluation
Regression Problems / Classification Problems / Clustering Problems
Regression Problems
SSE：The sum of squares due to error
MSE：Mean squared error
RMSE：Root mean squared error
MAE：Mean absolute error
MSE VS MAE
Mean Square Error, AKA, "Quadratic Loss",
"L2 Loss":
Mean Squared Error represents the average of the squared
difference between the original and predicted values in the data
set. It measures the variance of the residuals.
Mean Absolute Error, AKA, "L1 Loss":
The Mean absolute error represents the average of the absolute
difference between the actual and predicted values in the
dataset. It measures the average of the residuals in the
dataset.
MAE is more robust to data with outliers.
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R-square：Coefficient of determination
The lower value of MAE, MSE, and RMSE implies higher accuracy of
a regression model.
However, a higher value (closer to 1) of R square is considered
desirable.