🧭 Supervised ML — Quick References

This section provides practical, model-specific guides for key supervised machine learning algorithms.

These quick references outline model assumptions, tuning parameters, strengths, and limitations, helping analysts and data scientists choose the right approach for a given problem.

They are designed to support rapid model selection and ensure consistent application of best practices across projects.

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📂 Available Quick References

• 🧭 Naive Bayes Classifier QuickRef — A probabilistic model based on Bayes’ theorem with independence assumptions.
• 🧭 Decision Tree Classifier QuickRef — A non-parametric model for classification tasks, ideal for interpretable decision rules.
• 🧭 K-Nearest Neighbors (KNN) Classifier QuickRef — An instance-based method for classification using distance metrics.
• 🧭 Random Forest Classifier QuickRef — An ensemble of decision trees for improved accuracy and robustness.
• 🧭 Support Vector Machine (SVM) Classifier QuickRef — A powerful classifier using hyperplanes to separate data in high-dimensional space.