Last year, I wasted weeks building fraud detection.
Picked XGBoost because "everyone uses it." Got 94% accuracy. Shipped.
Two months later: catching nothing. Fraud patterns shifted. Model useless.
This TestLeaf blog changed how I think about ML.
The Real Problem
Wrong question: "Which algorithm is best?"
Right question: "What's the simplest model that meets my metric and stays reliable?"
Best model in 2026: Not the fanciest. The one that doesn't break in production.
The Workflow
Start Simple
Baseline: linear regression or logistic regression.
Fast, stable, interpretable. If it works, done. If not, benchmark.
Upgrade Thoughtfully
Tabular? Random Forest, then boosting.
Text? Naive Bayes, then upgrade if ROI justifies.
Images? Neural networks—if you have data/infrastructure.
Prevent Leakage
Clean splits > fancy algorithms.
One leaked feature = perfect testing, production failure.
Monitor Drift
Models degrade as the world changes.
Track metrics. Watch segments. Plan retraining.
Learning Types
Supervised: Have labels. Start here.
Unsupervised: No labels. Clustering, anomaly detection.
Reinforcement: Learn by acting. Complex for business.
Algorithms
Linear: Baseline.
Random Forests: Strong tabular default.
Gradient Boosting: Highest accuracy, sensitive to leakage.
Neural Networks: Unstructured data only.
k-NN: Similarity tasks, slow inference.
Naive Bayes: Fast text baseline.
My Process
Define task/metric/failures
Clean splits
Linear baseline
One robust upgrade
Compare across segments
Pick simplest
Deploy with monitoring
Use Cases
Churn: Logistic regression worked.
Fraud: Boosting helped, but threshold tuning mattered more.
Segmentation: k-means sufficient.
Anomaly: Isolation Forest + calibration.
What Changed
Stopped chasing "best" and started asking:
Meets metric?
Explainable?
Reliable under drift?
Operational cost?
Rebuilt fraud model: simpler boosting + better monitoring. Stable 8 months.
Want to detailed study, go through testleaf blog - Machine learning algorithms list
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