Evaluating model performance

Model performance refers to how well a model fits the given data and accurately predicts outcomes. It is important to evaluate model performance to assess its reliability and effectiveness in making predictions or in capturing the underlying patterns in the data. One commonly used metric to evaluate model performance is the R-squared value, also known as the coefficient of determination. R-squared measures the proportion of the variance in the dependent variable that can be explained by the independent variables in the model. A higher R-squared value indicates a better fit, as it means a larger proportion of the variability in the data is accounted for by the model.

However, R-squared alone may not provide a complete picture of model performance. Other metrics, such as mean squared error (MSE) or mean absolute error (MAE), can be used to assess the average prediction error of the model. Lower values of MSE or MAE indicate better predictive performance...