Supervised Learning

A comprehensive framework for supervised machine learning -- training predictive models on labeled datasets covering the full pipeline from problem formulation through deployment.

Sub-Skills

1. regression-models.md -- Predicting continuous values from input features
2. classification-methods.md -- Assigning inputs to predefined categories
3. model-evaluation.md -- Assessing generalization performance
4. feature-engineering.md -- Transforming raw data into predictive features
5. overfitting-prevention.md -- Techniques to ensure models generalize
6. ensemble-methods.md -- Combining models for improved accuracy
7. deep-learning.md -- Multi-layer neural networks for representation learning

Council

C28-CALCULUS, C10-CODEWEAVER, C25-PROMETHEUS, C14-KAIDO, C8-METASYNTH

Connections

• [[Skills/skills-master.md]]
• [[Skills/Quillan Skills Compendium.md]]
• [[classification-methods.md]]
• [[deep-learning.md]]
• [[ensemble-methods.md]]
• [[feature-engineering.md]]
• [[model-evaluation.md]]
• [[overfitting-prevention.md]]
• [[regression-models.md]]
• [[supervised_learning.md]]
• [[Quillan Knowledge files/17-Continuous Learning Paper.md]]
• [[Quillan Knowledge files/29-Recursive Introspection & Meta-Cognitive Self-Modeling.md]]
• [[00 - Meta/04 - Skills & Capabilities.md]]
• [[system prompts/Quillan-Samurai.md]]