RegressionCoeffsPlot

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

Purpose

The test visualizes regression coefficients along with their 95% confidence intervals to assess the impact of predictor variables on the response variable. This visualization offers insights into the variability and uncertainty of the model’s estimates, helping to understand the significance of each predictor.

Test Mechanism

The test involves extracting the estimated coefficients and their standard errors from the regression model. It then calculates the confidence intervals at a 95% confidence level, which indicates the range within which the true value is expected to fall 95% of the time. This information is visualized using a bar plot, where the x-axis represents predictor variables, the y-axis represents coefficients, and the error bars depict the confidence intervals.

Signs of High Risk

• The confidence interval contains the zero value, indicating insignificant contribution from the feature.
• Multiple coefficients exhibit confidence intervals containing zero, suggesting overall model reliability issues.
• Very wide confidence intervals, indicating high uncertainty in the coefficient estimates.

Strengths

• Provides a clear and comprehensible visualization of the significance and impact of predictor variables.
• Enables evaluation of uncertainty around each coefficient estimate through the inclusion of confidence intervals.

Limitations

• Assumes normality of residuals and independence of observations, which may not always hold true.
• Does not address multi-collinearity, which can impact the interpretation of coefficients.
• Limited to regression tasks and tabular datasets, and not applicable to other machine learning assignments or data structures.