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📋 Supervised ML Decision Cards

This section provides decision cards to support the selection, tuning, and evaluation of supervised machine learning models.
These resources are designed to help analysts and data scientists make informed choices based on dataset characteristics, project objectives, and trade-offs between interpretability, performance, and computational cost.
The cards cover model family selection, algorithm comparison, and criteria for deciding when to choose simpler or more complex approaches.

📂 Available Decision Cards

• 📋 Classifier Model Decision Card — Guide for selecting the most suitable classification algorithm based on data type, size, and performance goals.
• 📋 Naive Bayes vs KNN Trigger Card — Criteria for deciding between Naive Bayes and K-Nearest Neighbors in classification tasks.
• 📋 Tree vs Forest vs Boosted Trees Decision Card — When to use decision trees, random forests, or gradient-boosted tree models for optimal results.