Hubert Ronald

Appearance

Explainability Report ​

Version: v0.2

Sprint: Sprint 5 — Explainability

Executive Summary ​

The Explainability layer successfully generated reproducible SHAP explanations for all baseline predictive models implemented in RetainAI.

Supported models:

  • Logistic Regression
  • Random Forest
  • XGBoost

Each model produced:

  • global feature importance rankings;
  • SHAP beeswarm visualization;
  • representative local explanation;
  • serialized SHAP artifacts;
  • reusable metadata for downstream applications.

Cross-model observations ​

Several predictors consistently appear among the most influential variables across different model families.

Frequently recurring features include:

  • MonthlyIncome
  • OverTime
  • Age
  • JobLevel
  • TotalWorkingYears
  • YearsWithCurrManager
  • EnvironmentSatisfaction
  • JobSatisfaction
  • StockOptionLevel
  • DistanceFromHome

These variables therefore represent robust candidate drivers of employee attrition regardless of model architecture.

Logistic Regression ​

The linear baseline produces explanations that are highly interpretable.

Important variables include:

  • JobLevel
  • MonthlyIncome
  • TotalWorkingYears
  • YearsSinceLastPromotion
  • NumCompaniesWorked
  • OverTime
  • JobSatisfaction

These explanations align well with classical HR analytics literature and provide an interpretable reference model.

Random Forest ​

The Random Forest model distributes feature importance across a larger number of predictors.

Compared with Logistic Regression, the model assigns greater importance to:

  • StockOptionLevel
  • YearsAtCompany
  • DailyRate
  • HourlyRate

while still preserving the relevance of:

  • MonthlyIncome
  • Age
  • OverTime.

XGBoost ​

XGBoost produced the strongest nonlinear explanation patterns.

The most influential variables include:

  • OverTime
  • StockOptionLevel
  • MonthlyIncome
  • Age
  • NumCompaniesWorked
  • YearsWithCurrManager

The prominence of interaction-driven variables suggests that gradient boosting captures more complex relationships than linear models while remaining explainable through SHAP.

Explainability Artifacts ​

The Explainability pipeline generates:

  • SHAP values;
  • expected values;
  • feature importance tables;
  • beeswarm plots;
  • waterfall examples;
  • reusable metadata.

These artifacts are stored independently of model training and can be reused without recomputing SHAP values.

Future Integration ​

The Explainability artifacts constitute the foundation for subsequent RetainAI components.

Planned consumers include:

  • Streamlit Dashboard;
  • Monitoring layer;
  • Drift Detection;
  • Retention Advisor;
  • Amazon Bedrock integration.

Conclusion ​

It establishes a fully reproducible explainability workflow that transforms predictive models into transparent analytical assets.

The resulting artifacts are suitable for dashboard visualization, model auditing, AI-assisted recommendation systems, and future enterprise deployment.