A **model fitting norm** is an approach for evaluating the "goodness of fit" of predictive models (e.g. [[Linear Regression]]) to the underlying data. There is no single, best model-fitting norm, but some common examples are: - [[Mean Squared Error]] (MSE) - Root MSE - Lasso - Ridge - Elastic Net To apply these norms, you take each of the observed and predicted values and compute their error. Then sum the errors of all terms. Because errors between `observed` and `predicted` values can be positive or negative, and [[Errors add, not cancel]], we take steps to mitigate cancellation. Either: - **Absolute error** - taking the absolute value of the error - **Square error** - taking the square of the error - L1 (**Manhattan**) norm uses absolute error - This is called "manhattan" because it's like walking on vertical and horizontal tracks to find your error - L2 (**Euclidean**) norm uses square error - This takes the _hypotenuse_ to find the error - L`k` norm uses the `k`th exponent **** # More ## Source - Grad School ## Related - [[R2]]