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.