{
  "id": "lib-process:data-science-ml--experiment-planning",
  "_kind": "LibraryProcess",
  "_file": "generated-library/processes.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "experiment-planning",
    "description": "Experiment Planning and Hypothesis Testing - Design ML experiments with clear hypotheses,\nestablish statistical test criteria, plan A/B test configurations, and define success metrics with iterative learning loops.",
    "libraryPath": "library/specializations/data-science-ml/experiment-planning.js",
    "specialization": "data-science-ml",
    "references": [
      "- Rules of Machine Learning - Google: https://developers.google.com/machine-learning/guides/rules-of-ml\n- Experimentation Best Practices: https://developers.google.com/machine-learning/guides/rules-of-ml\n- A/B Testing Guidelines - Microsoft: https://exp-platform.com/\n- Statistical Power Analysis: https://www.stat.ubc.ca/~rollin/stats/ssize/"
    ],
    "example": "const result = await orchestrate('specializations/data-science-ml/experiment-planning', {\n  projectName: 'Recommendation Engine Improvement',\n  experimentGoal: 'Improve click-through rate by introducing collaborative filtering',\n  baselineModel: 'content-based-recommender-v1',\n  targetMetric: 'click_through_rate',\n  confidenceLevel: 0.95\n});",
    "usesAgents": [
      "general-purpose"
    ]
  },
  "outgoingEdges": [
    {
      "from": "lib-process:data-science-ml--experiment-planning",
      "to": "domain:data-science",
      "kind": "lib_applies_to_domain",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--experiment-planning",
      "to": "role:data-scientist",
      "kind": "lib_involves_role",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--experiment-planning",
      "to": "workflow:data-pipeline-deployment",
      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-process:data-science-ml--experiment-planning",
      "to": "specialization:data-science-ml",
      "kind": "lib_belongs_to_specialization",
      "attributes": {
        "weight": 0.9
      }
    }
  ],
  "incomingEdges": []
}