validmind.RegressionCoeffs

RegressionCoeffs

@tags('tabular_data', 'visualization', 'model_training')

@tasks('regression')

defRegressionCoeffs(model):

Assesses the significance and uncertainty of predictor variables in a regression model through visualization of coefficients and their 95% confidence intervals.

Purpose

The RegressionCoeffs metric visualizes the estimated regression coefficients alongside their 95% confidence intervals, providing insights into the impact and significance of predictor variables on the response variable. This visualization helps to understand the variability and uncertainty in the model's estimates, aiding in the evaluation of the significance of each predictor.

Test Mechanism

The function operates by extracting the estimated coefficients and their standard errors from the regression model. Using these, it calculates the confidence intervals at a 95% confidence level, which indicates the range within which the true coefficient value is expected to fall 95% of the time. The confidence intervals are computed using the Z-value associated with the 95% confidence level. The coefficients and their confidence intervals are then visualized in a bar plot. The x-axis represents the predictor variables, the y-axis represents the estimated coefficients, and the error bars depict the confidence intervals.

Signs of High Risk

  • The confidence interval for a coefficient contains the zero value, suggesting that the predictor may not significantly contribute to the model.
  • Multiple coefficients with confidence intervals that include zero, potentially indicating issues with model reliability.
  • Very wide confidence intervals, which may suggest high uncertainty in the coefficient estimates and potential model instability.

Strengths

  • Provides a clear visualization that allows for easy interpretation of the significance and impact of predictor variables.
  • Includes confidence intervals, which provide additional information about the uncertainty surrounding each coefficient estimate.

Limitations

  • The method assumes normality of residuals and independence of observations, assumptions that may not always hold true in practice.
  • It does not address issues related to multi-collinearity among predictor variables, which can affect the interpretation of coefficients.
  • This metric is limited to regression tasks using tabular data and is not applicable to other types of machine learning tasks or data structures.