[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"workflow-asset-6c515b5a":3,"seo:featured-workflow:6c515b5a-4ddd-11f1-9bc6-00163e2b0d79:en":83,"workflow-related-asset-6c515b5a-6c515b5a-4ddd-11f1-9bc6-00163e2b0d79":84},{"id":4,"uuid":5,"slug":6,"title":7,"description":8,"author_id":9,"author_name":10,"author_avatar":11,"token_estimate":12,"time_saved":12,"model_used":11,"fork_count":12,"vote_count":12,"view_count":12,"parent_id":12,"parent_uuid":11,"lang_type":13,"steps":14,"tags":21,"has_voted":27,"visibility":17,"share_token":11,"is_featured":12,"content_hash":28,"asset_kind":29,"target_tools":30,"install_mode":34,"entrypoint":18,"risk_profile":35,"dependencies":37,"verification":43,"agent_metadata":46,"agent_fit":59,"trust":71,"provenance":80,"created_at":82,"updated_at":82},3244,"6c515b5a-4ddd-11f1-9bc6-00163e2b0d79","asset-6c515b5a","TPOT — Automated Machine Learning with Genetic Programming","TPOT uses genetic programming to automatically design and optimize machine learning pipelines, selecting the best models and preprocessing steps from scikit-learn.","8a911193-3180-11f1-9bc6-00163e2b0d79","AI Open Source","",0,"en",[15],{"id":16,"step_order":17,"title":18,"description":11,"prompt_template":19,"variables":11,"depends_on":20,"expected_output":11},3807,1,"TPOT AutoML","# TPOT — Automated Machine Learning with Genetic Programming\n\n## Quick Use\n```bash\npip install tpot\npython -c \"\nfrom tpot import TPOTClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(\n    *load_iris(return_X_y=True), test_size=0.2)\nclf = TPOTClassifier(generations=5, population_size=20, verbosity=2)\nclf.fit(X_train, y_train)\nprint(clf.score(X_test, y_test))\nclf.export('best_pipeline.py')\n\"\n```\n\n## Introduction\nTPOT (Tree-based Pipeline Optimization Tool) automates the most tedious parts of machine learning by intelligently exploring thousands of possible pipeline configurations. It uses genetic programming to evolve scikit-learn pipelines, freeing data scientists from manual feature engineering and model selection.\n\n## What TPOT Does\n- Evolves complete ML pipelines using genetic programming\n- Automatically selects preprocessing, feature engineering, and model steps\n- Exports the best pipeline as a standalone Python script\n- Supports classification and regression tasks out of the box\n- Integrates with scikit-learn estimators and transformers\n\n## Architecture Overview\nTPOT represents each pipeline as a tree structure where nodes are scikit-learn operators. A genetic algorithm mutates, crosses over, and selects pipelines across generations. Fitness is evaluated via cross-validation. The final champion pipeline is exported as clean Python code using scikit-learn primitives.\n\n## Self-Hosting & Configuration\n- Install via pip with optional dependencies for XGBoost and DASK\n- Set generations and population_size to control search thoroughness\n- Use n_jobs=-1 to parallelize fitness evaluation across all cores\n- Enable DASK backend for distributed pipeline search on clusters\n- Configure scoring parameter to match your evaluation metric\n\n## Key Features\n- Zero-config AutoML that finds competitive pipelines automatically\n- Exports reproducible Python code rather than opaque model objects\n- Supports custom operator sets and search constraints\n- Built-in stacking ensemble capabilities\n- Warm-start to resume optimization from a previous run\n\n## Comparison with Similar Tools\n- **AutoGluon** — broader scope with tabular, text, and image; TPOT focuses on scikit-learn pipeline optimization\n- **auto-sklearn** — also optimizes sklearn pipelines but uses Bayesian optimization; TPOT uses genetic programming\n- **FLAML** — faster search via cost-frugal tuning; TPOT explores more pipeline structures\n- **H2O AutoML** — requires the H2O server; TPOT runs in pure Python\n\n## FAQ\n**Q: How long does TPOT take to run?**\nA: Depends on dataset size and generations setting. Small datasets can finish in minutes; large ones may need hours. Use max_time_mins to set a budget.\n\n**Q: Can TPOT use GPUs?**\nA: TPOT itself is CPU-based, but you can include XGBoost with GPU support as a custom operator.\n\n**Q: Does TPOT support deep learning?**\nA: TPOT focuses on traditional ML pipelines. 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