---

name: kaggle-linear-models description: "Safe, modern Kaggle-style SVM and logistic-regression workflows for dense matrices, hashed sparse CSVs, and one-hot categorical tables." disable-model-invocation: true metadata: disco-role: operating license: MIT

Kaggle Linear Models

Use this sub-skill for classical tabular machine-learning tasks that look like Kaggle starter code and should stay bounded, reproducible, and Python 3 friendly.

Choose a route

• Dense numeric matrix plus class labels: use scripts/sklearn_svm_submission.py.
• Criteo-like sparse CSV with Label and Id columns: use scripts/hashed_logistic_sgd.py.
• Categorical table with ACTION/id columns: use scripts/categorical_logistic_submission.py.
• Synthetic smoke fixtures for all helpers: use scripts/make_tiny_fixtures.py.

Read references/workflows.md for runnable recipes, references/data-formats.md when the CSV layout is unclear, references/api-reference.md for current scikit-learn usage, and references/troubleshooting.md when a run fails.

What this sub-skill includes

• Dense-matrix SVC submissions with configurable SVC arguments.
• Hashed online logistic regression for Criteo-like sparse categorical CSVs.
• One-hot categorical logistic regression with optional bounded cross-validation.
• Interaction-feature guidance from the expanded Amazon-style logistic example, kept reference-only unless the user explicitly asks for a bounded experiment.

What stays out

• Admissions logistic regression with statsmodels. That route is owned by statsmodels-logit-workflow.
• Live Twitter/X ingestion or JSON streaming. That route is owned by twitter-json-workflow.
• Exhaustive greedy feature selection over a large feature lattice.
• Any workflow that depends on the original repository checkout.

If a user asks for one of those excluded routes, point them to the sibling skill that owns that route instead of extending this one.

Quick selection rules

1. If the input is a dense numeric matrix with a separate label file, start with sklearn_svm_submission.py.
2. If the input is a sparse tabular CSV with Label/Id columns and mixed string or numeric feature values, start with hashed_logistic_sgd.py.
3. If the input is a categorical CSV with ACTION/id columns, start with categorical_logistic_submission.py.
4. If you need tiny fixtures to smoke-test the helpers, generate them first with make_tiny_fixtures.py.

Expected behavior

• All bundled scripts accept explicit paths and --help.
• The helpers create missing parent directories for outputs.
• The modernized categorical workflow uses OneHotEncoder(handle_unknown="ignore", sparse_output=True).
• The categorical workflow uses bounded StratifiedKFold cross-validation only when you request it.
• The interaction-feature recipe is documentation only by default; it should not be promoted into a default script unless the user gives a small dataset and a clear budget.

When to read each bundled reference

• references/workflows.md: end-to-end command recipes and the reference-only interaction-feature pattern.
• references/data-formats.md: required columns, label conventions, and output layouts.
• references/api-reference.md: current sklearn signatures and API replacements for removed legacy names.
• references/troubleshooting.md: old sklearn API errors, unseen categories, single-class folds, large feature spaces, and the broken v2 hashed script.

When to run each bundled helper

• scripts/make_tiny_fixtures.py: create small dense, hashed, and categorical fixtures for smoke tests.
• scripts/sklearn_svm_submission.py: fit an SVC on dense numeric matrices and write class predictions.
• scripts/hashed_logistic_sgd.py: fit an online hashed logistic model on Criteo-like CSVs and write probabilities.
• scripts/categorical_logistic_submission.py: fit a modern sparse one-hot logistic model on categorical CSVs and optionally report bounded CV AUC.