Supervised Learning Engineer

Expert in supervised learning pipelines — classification, regression, model selection, hyperparameter tuning, and production deployment with scikit-learn, XGBoost, and LightGBM.

Gold

v1.0.10 activationsAI & Machine LearningTechnologyintermediate

SupaScore

84.8

Research Quality (15%)

8.3

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.7

Completeness (10%)

8.2

User Satisfaction (20%)

8.5

Decision Usefulness (15%)

8.4

Best for

▸Building scikit-learn pipelines with feature engineering and model selection for tabular data
▸XGBoost hyperparameter optimization for gradient boosting classification problems
▸Production deployment of supervised ML models with monitoring and rollback capabilities
▸Cross-validation strategy design and bias-variance analysis for model evaluation
▸Ensemble model stacking with multiple algorithms for improved prediction accuracy

What you'll get

●Complete scikit-learn Pipeline with ColumnTransformer, feature scaling, and optimized XGBoost classifier with cross-validated hyperparameters
●Model comparison report showing performance metrics across linear models, random forests, and gradient boosting with bias-variance analysis
●Production deployment code with model versioning, monitoring, and rollback procedures for supervised learning pipeline

Not designed for ↓

×Deep learning neural networks or transformer architectures
×Unsupervised learning like clustering or dimensionality reduction
×Time series forecasting with temporal dependencies
×Computer vision or natural language processing tasks

Expects

Tabular dataset with clear target variable, business context, and performance requirements (latency, interpretability, accuracy).

Returns

Production-ready scikit-learn pipeline with optimized hyperparameters, evaluation metrics, and deployment recommendations.

Evidence Policy

Standard: no explicit evidence policy.

supervised-learningscikit-learnxgboostclassificationregressionmachine-learning

Research Foundation: 6 sources (2 paper, 2 official docs, 1 industry frameworks, 1 books)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.12/15/2026

Auto-versioned: masterfile quality gate passed (score: 85)

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

Feature Engineering StrategistGold Hyperparameter Tuning ExpertGold ML Model Evaluation ExpertGold Model Deployment OptimizerPlatinum XGBoost Mastery ExpertGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

AutoML Pipeline DesignerGold MLOps Platform EngineerGold Feature Store ArchitectGold

Common Workflows

ML Model Production Pipeline

End-to-end workflow from model development through evaluation to production deployment with monitoring

supervised-learning-engineer→ML Model Evaluation Expert→Model Deployment Optimizer→MLOps Platform Engineer

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