Individual skill
matlab-classify-tabular-data
Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
skills-catalog/ai-and-statistics/matlab-classify-tabular-data/SKILL.md
Source commit 032b4b605999b1f4adecaac1efa1c9faad41adf6
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 3, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
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Repository context
- Repository stars (not skill ratings)
- 1,275
- Repository last updated
- Nov 5, 2026
- License
- NOASSERTION
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- claude-code Repository topic
- codex-plugin Repository topic
- github-copilot Repository topic
- matlab Repository topic
- matlab-mcp-server Repository topic
- engineering-agents Repository topic
- codex Repository topic
- codex-skills Repository topic
- engineering Derived category
- data Derived category
- education Derived category
- testing Derived category
- testing-browser Derived category
Files and excerpt
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Browse files
SKILL.mdcore · 36,951 bytesSKILL.mdcore · 36,951 bytesSKILL.mdcore · 36,951 bytesSKILL.mdcore · 36,951 bytesmanifest.yamlasset · 524 bytesreferences/README.mddoc · 3,217 bytesreferences/dataprep.mddoc · 7,666 bytesreferences/hpo.mddoc · 11,785 bytesreferences/save-and-export.mddoc · 7,136 bytesreferences/select-classifiers-imbalanced.mddoc · 3,039 bytesreferences/select-classifiers.mddoc · 9,592 bytesscripts/README.mddoc · 2,322 bytesscripts/helpers/aggregate_nested_cv_loss.masset · 470 bytesscripts/helpers/apply_preproc.masset · 4,197 bytesscripts/helpers/build_model_definitions.masset · 16,994 bytesscripts/helpers/compute_data_flags.masset · 2,836 bytesscripts/helpers/compute_pairwise_pvalues_cv.masset · 623 bytesscripts/helpers/compute_pairwise_pvalues_holdout.masset · 556 bytesscripts/helpers/count_categorical_levels.passet · 376 bytesscripts/helpers/export_workflow_script.masset · 15,583 bytesscripts/helpers/find_zero_variance_columns.masset · 810 bytesscripts/helpers/is_bag_of_tokens.masset · 181 bytesscripts/helpers/make_holdout_split.masset · 387 bytesscripts/helpers/plot_accuracy_bars.masset · 994 bytesscripts/helpers/plot_boosting_curve.masset · 1,243 bytesscripts/helpers/plot_pvalue_heatmap.masset · 465 bytesscripts/helpers/plot_training_time.masset · 797 bytesscripts/helpers/resolve_recipe.masset · 1,084 bytesscripts/helpers/resume_boosting_cv.masset · 543 bytesscripts/helpers/resume_boosting_holdout.masset · 448 bytesscripts/helpers/retrain_with_hpo.masset · 2,777 bytesscripts/helpers/retrain_with_resume.masset · 2,573 bytesscripts/helpers/sanitize_model_name.masset · 799 bytesscripts/helpers/save_selected_models.masset · 4,098 bytesscripts/helpers/score_holdout_test.masset · 278 bytesscripts/helpers/stratified_subsample.passet · 367 bytesscripts/helpers/train_and_score_cv.masset · 405 bytesscripts/helpers/train_and_score_holdout.masset · 1,157 bytesscripts/model_catalog/branch_categorical.masset · 4,277 bytesscripts/model_catalog/branch_many_missing.masset · 3,132 bytesscripts/model_catalog/branch_regular.masset · 4,239 bytesscripts/model_catalog/branch_sparse.masset · 1,347 bytesscripts/model_catalog/branch_wide.masset · 2,039 bytesscripts/model_catalog/classifier_branches.masset · 2,262 bytesscripts/model_catalog/classifier_thresholds.masset · 2,205 bytesscripts/model_catalog/imbalanced_boosting.masset · 3,043 bytesscripts/model_catalog/model_recipe.masset · 3,580 bytes
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