Automate AutoGluon TabularPredictor workflows from construction to deployment.
npx skills add https://github.com/crossxwill/IML4Finance --skill run-autogluon
Please help me install this Agent Skill. Skill: run-autogluon Source: https://github.com/crossxwill/IML4Finance/tree/main/.github/skills/run-autogluon Command: npx skills add https://github.com/crossxwill/IML4Finance --skill run-autogluon
Consolidated AutoGluon skill covering end-to-end TabularPredictor workflow (constructor, fit, predict_proba, fit_summary, save/load, set_model_best), binary threshold calibration and setting, sklearn wrapper integration, and monotonic constraints. Use for any AutoGluon Tabular questions, training/ensembling configuration, inference/probabilities, threshold tuning, deployment persistence, or sklearn interoperability.
Train and evaluate an AutoGluon TabularPredictor end-to-end on your dataset, then calibrate thresholds, wrap for sklearn usage if needed, and persist the best model.
High-intent search queries and answers about installing and using this skill.
Train an AutoGluon TabularPredictor by initializing the constructor, calling fit on your tabular dataset, and evaluating results with fit_summary. This workflow covers binary/multiclass classification and regression tasks end-to-end.
Yes, AutoGluon supports binary threshold calibration and setting workflows. You can calibrate decision thresholds, use set_model_best to designate a preferred model, and evaluate performance using fit_summary or leaderboard outputs.
AutoGluon provides sklearn wrapper integration for deployment and interoperability. This allows you to wrap trained TabularPredictor models for use within sklearn pipelines and standard production inference contexts.
AutoGluon TabularPredictor supports save and load persistence workflows. After training and calibrating your model, you can save it to disk and reload it later to generate predictions or predict_proba in production environments.
AutoGluon TabularPredictor supports monotonic constraints during model training. This allows you to enforce directional relationships between specific input features and the target variable across binary/multiclass classification and regression tasks.
AutoGluon workflows assume data is organized in a Data/ directory and lectures or workflows reside in Lectures/. This structure aligns with course materials and lab setups for tabular model training and evaluation.
Vishnugupta2711Train XGBoost and LightGBM models on tabular datasets with early stopping and cross-validation.
rubenslivLoad, fine-tune, and run inference with pre-trained transformer models.
infantesromeroadrianOrganize ML projects with Cookiecutter, sklearn pipelines, and OOP patterns.
nahisahoDesign and evaluate ensemble methods for tabular classification and regression.
