Evaluation Metrics: R2

 Evaluation Metrics: R2

Evaluation metrics are essential tools for assessing the performance of machine learning models, particularly in regression tasks where you're predicting continuous values. Here are descriptions of three common evaluation metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2).

R-squared (R2):

R2 tells you how well your model fits the data. It's like a score between 0 and 1, where 1 means your model is perfect, and 0 means it's as good as just using the average of the actual values. R2 represents the proportion of the variation in the data that your model can explain. Higher R2 values indicate a better model fit.