{"id":19849540,"url":"https://github.com/filipspl/bayesian-rf-knime-scikit","last_synced_at":"2026-04-12T16:05:48.242Z","repository":{"id":92209098,"uuid":"220450610","full_name":"filipsPL/bayesian-rf-knime-scikit","owner":"filipsPL","description":"Bayesian optimization of RF via scikit in KNIME","archived":false,"fork":false,"pushed_at":"2019-11-08T13:09:56.000Z","size":1145,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-28T23:10:34.024Z","etag":null,"topics":["bayesian-optimization","knime","knime-analytics-platform","python","random-forest","scikit-learn"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/filipsPL.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-11-08T11:12:56.000Z","updated_at":"2021-02-20T00:20:22.000Z","dependencies_parsed_at":"2023-06-08T00:07:05.463Z","dependency_job_id":null,"html_url":"https://github.com/filipsPL/bayesian-rf-knime-scikit","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/filipsPL/bayesian-rf-knime-scikit","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/filipsPL%2Fbayesian-rf-knime-scikit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/filipsPL%2Fbayesian-rf-knime-scikit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/filipsPL%2Fbayesian-rf-knime-scikit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/filipsPL%2Fbayesian-rf-knime-scikit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/filipsPL","download_url":"https://codeload.github.com/filipsPL/bayesian-rf-knime-scikit/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/filipsPL%2Fbayesian-rf-knime-scikit/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262981454,"owners_count":23394534,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-optimization","knime","knime-analytics-platform","python","random-forest","scikit-learn"],"created_at":"2024-11-12T13:21:30.913Z","updated_at":"2026-04-12T16:05:48.215Z","avatar_url":"https://github.com/filipsPL.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# bayesian-rf-knime-scikit\n\nBayesian Optimization of RandomForest parameters, with scikit-learn, to be used in KNIME in Python learner node. Based on https://github.com/fmfn/BayesianOptimization/ by fmfn.\n\nPrerequisities:\n- `pip install bayesian-optimization`\n\n\nWhy?\n1. Parameter Optimization Loop Node(s) doesn't work as expected for some data. Including Bayesian optimization.\n2. You may want to use scikit-learn instead of KNIME or Weka implementation.\n3. You can tune this workflow to optimize other parameters for many different scikit algorithms.\n\n## Setup\n\n- In python node please select python2.\n- copy\u0026paste the python code into the code window of Python Learner (`python-learner.py`) and Python Predictor (`python-predictor.py`)\n- sample workflow:\n\n![](obrazki/README-94e22874.png)\n\n- in the input table, the class should be in the last column\n- fine tuning - edit variables at the top of the `python-learner.py`:\n\n```python\n#\n# Bounded region of parameter space\n#\n\nparameterDict = { 'n_estimators': (100, 1200),\n            'max_depth': (5, 30),\n            'min_samples_split': (2, 100),\n            'min_samples_leaf': (1, 10)\n            }\n\n#\n# bayesian configuration\n#\n\ninit_points = 5\nn_iter = 20\n```\n\n- please note: scripts (after slight modifications) can be run from the command line\n- sample data file provided (`nr-ahr-lite.csv ` from my [tox21 dataset](https://github.com/filipsPL/tox21_dataset))\n\n\n## Standard output\n\nAmong some training progress data (static) info about best parameters found is displayed:\n\n```\nBest params: {'min_samples_split': 2, 'n_estimators': 205, 'max_depth': 30, 'min_samples_leaf': 1}\nBest target value: 0.837006427916\n```\n\n## ROC output (ROC curve node)\n\n![](obrazki/README-5f63414c.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilipspl%2Fbayesian-rf-knime-scikit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffilipspl%2Fbayesian-rf-knime-scikit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilipspl%2Fbayesian-rf-knime-scikit/lists"}