"algo-hr-turnover"

Employee Turnover Prediction

Overview

Turnover prediction uses classification models (logistic regression, random forest, XGBoost) to estimate the probability an employee will leave within a defined period (typically 6-12 months). Features include tenure, compensation, performance, promotion history, and engagement signals.

When to Use

Trigger conditions:

• Identifying employees at high risk of voluntary departure
• Quantifying which factors drive turnover for targeted interventions
• Prioritizing retention budgets toward highest-impact employees

When NOT to use:

• For involuntary termination planning (different process and ethics)
• When headcount is < 200 (insufficient data for reliable modeling)

Algorithm

IRON LAW: Turnover Models Predict RISK, Not Certainty
A predicted 80% turnover probability means "employees with similar
profiles historically left 80% of the time." It does NOT mean this
specific employee WILL leave. Never use model outputs as sole basis
for employment decisions — that creates legal and ethical liability.

Phase 1: Input Validation

Collect: employee demographics, tenure, compensation (relative to market), last promotion date, performance ratings, manager change history, engagement survey scores, commute distance. Outcome: voluntary departure within N months. Gate: Minimum 200 turnover events, features available before departure date.

Phase 2: Core Algorithm

1. Feature engineering: tenure buckets, comp ratio (salary/market median), time since last promotion, manager tenure, engagement trend
2. Handle class imbalance: turnover rate typically 10-20%. Use SMOTE or class weights.
3. Train: logistic regression (interpretable, HR-preferred) or GBDT (higher accuracy)
4. Output: probability of departure + top risk factors per employee

Phase 3: Verification

Evaluate: AUC, precision-recall (at actionable thresholds). Backtest: did the model correctly flag employees who left in the past 6 months? Gate: AUC > 0.70, precision > 50% at top decile.