RegressionModelOutsampleComparison

Computes MSE and RMSE for multiple regression models using out-of-sample test to assess model’s prediction accuracy on unseen data.

Purpose: The RegressionModelOutsampleComparison test is designed to evaluate the predictive performance of multiple regression models by means of an out-of-sample test. The primary aim of this test is to validate the model’s ability to generalize to unseen data, a common challenge in the context of overfitting. It does this by computing two critical metrics — Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), which provide a quantifiable measure of the model’s prediction accuracy on the testing dataset.

Test Mechanism: This test requires multiple models (specifically Ordinary Least Squares - OLS regression models) and a test dataset as inputs. Each model generates predictions using the test dataset. The residuals are then calculated and used to compute the MSE and RMSE for each model. The test outcomes, which include the model’s name, its MSE, and RMSE, are recorded and returned in a structured dataframe format.

Signs of High Risk: - High values of MSE or RMSE indicate significant risk, signifying that the model’s predictions considerably deviate from the actual values in the test dataset. - Consistently large discrepancies between training and testing performance across various models may indicate an issue with the input data itself or the model selection strategies employed.

Strengths: - This test offers a comparative evaluation of multiple models’ out-of-sample performance, enabling the selection of the best performing model. - The use of both MSE and RMSE provides insights into the model’s prediction error. While MSE is sensitive to outliers, emphasizing larger errors, RMSE provides a more interpretable measure of average prediction error given that it’s in the same unit as the dependent variable.

Limitations: - The applicability of this test is limited to regression tasks, specifically OLS models. - The test operates under the assumption that the test dataset is a representative sample of the population. This might not always hold true and can result in less accurate insights. - The interpretability and the objectivity of the output (MSE and RMSE) can be influenced when the scale of the dependent variable varies significantly, or the distribution of residuals is heavily skewed or contains outliers.