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name: classical-algorithms description: "Explains ML Glossary classical algorithm families including decision trees, KNN, random forests, boosting, SVM, regression variants, clustering placeholders, and reinforcement-learning basics." disable-model-invocation: true metadata: disco-role: operating license: MIT

Classical Algorithms

Use this sub-skill for ML Glossary tasks about non-neural model families and algorithm comparisons. It covers decision trees, K-nearest neighbors, random forests, boosting, SVMs, ordinary/polynomial/lasso/ridge/stepwise regression, clustering placeholder status, and introductory reinforcement-learning vocabulary.

Read when

• The user asks to compare classical ML classifiers or regressors.
• The task mentions ID3, C4.5, CART, information gain, gain ratio, Gini, KNN, Euclidean distance, random forests, bagging, boosting, AdaBoost-style error focus, SVM margins, kernel trick, OLS, polynomial regression, lasso, ridge, stepwise/spline regression, clustering categories, or Q-learning.
• A documentation task needs a beginner-friendly algorithm entry or caveat about placeholder sections.
• The user asks whether a classical algorithm has an illustrative source snippet in the ML Glossary lineage.

Main references

• references/topic-map.md contains the self-contained classical algorithm map and comparison tables.
• references/troubleshooting.md covers algorithm-selection mistakes, placeholder pages, and legacy code caveats.
• scripts/knn_demo.py is a safe pure-Python KNN demo adapted from the original source idea.

Fast routing

Task	Use
Compare ID3/C4.5/CART	topic-map.md decision-tree section.
Explain KNN in classification and regression	topic-map.md plus scripts/knn_demo.py.
Compare random forests, bagging, and boosting	topic-map.md ensemble section.
Explain SVM hyperplanes, support vectors, margins, and kernel trick	topic-map.md SVM section.
Explain regression variants such as OLS, lasso, ridge, polynomial	topic-map.md regression section, then cross-link to basics for formulas.
Explain Q-learning or RL vocabulary at a glossary level	topic-map.md reinforcement-learning section.
Fill in clustering or applications placeholders	troubleshooting.md placeholder guidance and root ../../references/resources-catalog.md.

Workflow for answers

1. Identify the prediction type: classification, regression, clustering/unsupervised, or reinforcement learning.
2. Give the intuition first: nearest neighbors, tree splits, ensemble voting/averaging, margins, or reward-maximizing policy.
3. Explain required data assumptions: labeled data for supervised classifiers/regressors, distance metric and k for KNN, split criteria for trees, weak learners for boosting, kernel/feature space for SVM.
4. If code is requested, use the bundled knn_demo.py for KNN. For trees/random forests, describe the algorithm and cite legacy caveats rather than presenting source code as production-ready.
5. Route math-heavy logistic regression, gradient descent, and evaluation metrics back to ../basics-and-math/SKILL.md.
6. Route neural-network classifier comparisons to ../neural-networks/SKILL.md after covering the classical side.

Boundaries

This sub-skill owns:

• Classical supervised algorithm explanations and comparisons.
• The repo's non-neural algorithm-page status, including placeholders.
• Introductory RL glossary content from the repository.
• Safe KNN demonstration.

Route elsewhere:

• Logistic-regression derivations, sigmoid/log loss, MSE, gradient descent formulas → ../basics-and-math/SKILL.md.
• Neural-network architectures or deep classifiers → ../neural-networks/SKILL.md.
• Library/dataset/paper recommendations for algorithm practice → ../../references/resources-catalog.md.
• Sphinx authoring and build maintenance → ../../references/site-maintenance.md.

Quality checks

• Do not overstate placeholder sections: clustering pages and several RL subsections were incomplete.
• Do not claim legacy tree/random-forest source files are polished APIs; they were evidence for explanations.
• When comparing algorithms, tie the choice to data shape, interpretability, feature scaling, noise/outliers, nonlinearity, and computational cost.
• If the user needs modern scikit-learn commands, label them as modern external guidance rather than repo-grounded API facts.