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name: supervised-and-tabular-models description: "Routes numpy-ml tabular estimator, tree, nonparametric, and matrix-factorization tasks with fit/predict guidance and compatibility checks." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Supervised and Tabular Models

Use this sub-skill when a task asks how to use, inspect, or troubleshoot numpy-ml estimators for small NumPy tabular problems:

• linear and generalized linear models;
• logistic regression and Gaussian naive Bayes;
• decision trees, random forests, and gradient-boosted decision trees;
• KNN, kernel regression, and Gaussian process regression;
• alternating least-squares and non-negative matrix factorization.

This package is an educational NumPy/SciPy implementation library, not a scikit-learn replacement. Prefer small, explicit arrays and inspect learned attributes when validating behavior.

First Checks

1. Confirm the runtime uses the root skill compatibility guidance: Python 3.8 is the verified legacy path, with numpy<1.24 and SciPy installed.

2. Run the local helper when a task needs a quick estimator sanity check:

   python sub-skills/supervised-and-tabular-models/scripts/tabular_smoke.py
   

3. Read references/api-reference.md before writing code that depends on constructor defaults, fit/predict methods, model attributes, or estimator-family differences.

4. Read references/workflows.md for copyable tiny examples and model-selection routes.

5. Read references/troubleshooting.md when results contain nan, shapes do not match, fit returns None, class labels behave unexpectedly, or legacy dependency errors appear.

Route by Task

User asks for	Use this route
ordinary least squares, weighted/incremental least squares, ridge, Bayesian regression	Linear-model guidance in api-reference.md and workflows.md.
binary logistic regression, Gaussian naive Bayes, GLM links	Classification/GLM rows in api-reference.md; validate target shape and probability outputs.
CART, random forest, GBDT	Tree-family guidance; keep tiny data deterministic and set seeds where available.
KNN, Nadaraya-Watson kernel regression, Gaussian process regression	Nonparametric route; check metric/kernel object strings and prediction return shapes.
matrix factorization, ALS/NMF	Factorization route; inspect .W and .H after in-place fit.
feature standardization, one-hot encoding, tokenization, kernels, distances, graph helpers	Route to ../preprocessing-and-utilities/SKILL.md.
neural-network layers, losses, optimizers, schedulers	Route to ../neural-network-components/SKILL.md.
GMM/HMM/LDA/n-gram language models	Route to ../probabilistic-and-sequence-models/SKILL.md.

Operating Notes

• Most estimator fit methods mutate the model and return None; keep the estimator object and inspect attributes such as .beta, .parameters, .W, .H, tree nodes, or trained kernel state.
• Methods are implemented for small educational experiments; avoid promising production-scale training, automatic validation, scikit-learn estimator protocol completeness, or GPU acceleration.
• Original repository tests compare some outputs against scikit-learn or other libraries, but those comparison packages are optional and are not runtime dependencies for this skill.
• If a workflow begins with raw text, labels, or feature dictionaries, first route preprocessing to ../preprocessing-and-utilities/SKILL.md, then return here for estimator fitting.