The TD_RegressionEvaluator function computes metrics to evaluate and compare multiple models and summarizes how close predictions are to their expected values. It takes the actual and predicted values of the dependent variables to calculate specified metrics, you choose which metrics you want to calculate from a list of supported metrics.
Regression is a type of machine learning algorithm that aims to establish a relationship between a dependent variable and one or more independent variables. Use regression to predict the value of a dependent variable based on the values of independent variables. The most common type of regression is linear regression, where the relationship between variables is assumed to be linear.
In the regression process, a model is trained on a dataset consisting of input variables and corresponding output variables. The model tries to find the best fit line or curve that passes through the data points minimizing the difference between the actual and predicted values.
- Mean squared error (MSE).
- Mean absolute error (MAE).
Selecting the appropriate features or independent variables is one crucial aspect of regression. Too many features can lead to overfitting, where the model performs well on the training data but poorly on the test data. Alternatively, too few features can lead to underfitting, where the model fails to capture the underlying patterns in the data.