Metrics you can use to evaluate your Regression Model: 1. R Square (Coefficient of Determination) - This metric explains the percentage of variance explained by covariates in the model. It ranges between 0 and 1. But it's a good practice to consider adjusted R² than R² to determine model fit. 2. Adjusted R² - The problem with R² is that it keeps on increasing as you increase the number of variables, regardless of the fact that the new variable is actually adding new information to the model. To overcome that, we use adjusted R² which doesn't increase (stays same or decrease) unless the newly added variable is truly useful. 3. F Statistics - It evaluates the overall significance of the model, It is the ratio of explained variance by the model by unexplained variance. It compares the full model with an intercept only (no predictors) model. Its va...
