RegressionResidualsPlot

Evaluates regression model performance using residual distribution and actual vs. predicted plots.

Purpose: The RegressionResidualsPlot metric aims to evaluate the performance of regression models. By generating and analyzing two plots – a distribution of residuals and a scatter plot of actual versus predicted values – this tool helps to visually appraise how well the model predicts and the nature of errors it makes.

Test Mechanism: The process begins by extracting the true output values (y_true) and the model’s predicted values (y_pred). Residuals are computed by subtracting predicted from true values. These residuals are then visualized using a histogram to display their distribution. Additionally, a scatter plot is derived to compare true values against predicted values, together with a “Perfect Fit” line, which represents an ideal match (predicted values equal actual values), facilitating the assessment of the model’s predictive accuracy.

Signs of High Risk: - Residuals showing a non-normal distribution, especially those with frequent extreme values. - Significant deviations of predicted values from actual values in the scatter plot. - Sparse density of data points near the “Perfect Fit” line in the scatter plot, indicating poor prediction accuracy. - Visible patterns or trends in the residuals plot, suggesting the model’s failure to capture the underlying data structure adequately.

Strengths: - Provides a direct, visually intuitive assessment of a regression model’s accuracy and handling of data. - Visual plots can highlight issues of underfitting or overfitting. - Can reveal systematic deviations or trends that purely numerical metrics might miss. - Applicable across various regression model types.

Limitations: - Relies on visual interpretation, which can be subjective and less precise than numerical evaluations. - May be difficult to interpret in cases with multi-dimensional outputs due to the plots’ two-dimensional nature. - Overlapping data points in the residuals plot can complicate interpretation efforts. - Does not summarize model performance into a single quantifiable metric, which might be needed for comparative or summary analyses.