Models & Algorithms
Algorithm options, tradeoffs, and recommended evolution path
OverviewData & FeaturesModels & AlgorithmsDecisioning PipelineReal-time ArchitectureExplainabilityFeedback LoopMetrics & Impact
Complexity vs Business Impact
Gradient Boosting (XGBoost/LightGBM)
Recommended BaselineTree ensemble for high accuracy on tabular data
Pros
- High accuracy
- Handles non-linear interactions
- Works well with tabular data
- Feature importance
Cons
- Less interpretable
- Requires feature engineering
- Hyperparameter tuning
Recommended Evolution Path
1
Rules-Based
Quick start
2
Gradient Boosting
High accuracy
3
Learning-to-Rank
Ranking optimization
4
Reinforcement Learning
Long-term value
Key Insight
Start with Gradient Boosting as the baseline—it offers the best accuracy-to-complexity ratio. Evolve to Learning-to-Rank when you have enough ranking data, then explore RL for long-term LTV optimization.