computer-science-processes-database/processes/machine_learning/machine_learning-hyperparameter-optimization.json

{
  "id": "machine_learning-hyperparameter-optimization",
  "name": "Hyperparameter Optimization",
  "category": "computer_science",
  "subcategory": "machine_learning",
  "subcategory_name": "Machine Learning",
  "description": "Hyperparameter Optimization process visualization. This process flowchart outlines key steps, checks, and outputs.",
  "complexity": {
    "nodes": 12,
    "edges": 13,
    "conditionals": 2,
    "logicGates": {
      "orGates": 3,
      "andGates": 1,
      "notGates": 0,
      "total": 4
    },
    "level": "high",
    "detailLevel": "source_grounded_rebuild",
    "loops": 1
  },
  "colorScheme": {
    "red": {
      "hex": "#ff6b6b",
      "category": "Triggers & Inputs"
    },
    "yellow": {
      "hex": "#ffd43b",
      "category": "Structures & Objects"
    },
    "green": {
      "hex": "#51cf66",
      "category": "Processing & Operations"
    },
    "blue": {
      "hex": "#74c0fc",
      "category": "Intermediates & States"
    },
    "violet": {
      "hex": "#b197fc",
      "category": "Products & Outputs"
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  },
  "mermaid": "graph TD\n    N1[\"Hyperparameter Optimization...\"]\n    N2[\"Model Family\"]\n    N3[\"Metric + Budget\"]\n    N4[\"Search Space\"]\n    N5[\"Sampler Strategy\"]\n    N6[\"Propose Params\"]\n    N7[\"Train Candidate\"]\n    N8{\"Validate Candidate\"}\n    N9[\"Trial History\"]\n    N10[\"Best Parameters\"]\n    N11{\"Source-grounded check: Pattern...\"}\n    N12[\"Hyperparameter Optimization...\"]\n\n    N1 --> N2\n    N2 --> N3\n    N3 --> N4\n    N4 --> N5\n    N5 --> N6\n    N6 --> N7\n    N7 --> N8\n    N8 -->|yes| N9\n    N9 --> N10\n    N10 --> N11\n    N11 -->|yes| N12\n    N8 -->|iterate| N3\n    N4 -->|skip/opt| N7\n\n    style N1 fill:#ff6b6b,color:#fff\n    style N2 fill:#ff6b6b,color:#fff\n    style N3 fill:#ff6b6b,color:#fff\n    style N4 fill:#ffd43b,color:#000\n    style N5 fill:#ffd43b,color:#000\n    style N6 fill:#51cf66,color:#fff\n    style N7 fill:#51cf66,color:#fff\n    style N8 fill:#51cf66,color:#fff\n    style N9 fill:#74c0fc,color:#fff\n    style N10 fill:#b197fc,color:#fff\n    style N11 fill:#ffd43b,color:#000\n    style N12 fill:#b197fc,color:#fff",
  "sources": [
    {
      "title": "Pattern Recognition and Machine Learning",
      "authors": "Bishop, C. M.",
      "journal": "Springer",
      "year": "2006",
      "pubmed": null,
      "doi": null,
      "url": "https://link.springer.com/book/9780387310732"
    },
    {
      "title": "The Elements of Statistical Learning",
      "authors": "Hastie, T.; Tibshirani, R.; Friedman, J.",
      "journal": "Springer",
      "year": "2009",
      "pubmed": null,
      "doi": "10.1007/978-0-387-84858-7",
      "url": "https://doi.org/10.1007/978-0-387-84858-7"
    },
    {
      "title": "Deep Learning",
      "authors": "Goodfellow, I.; Bengio, Y.; Courville, A.",
      "journal": "MIT Press",
      "year": "2016",
      "pubmed": null,
      "doi": null,
      "url": "https://www.deeplearningbook.org/"
    }
  ],
  "keywords": [
    "hyperparameter",
    "optimization"
  ],
  "relatedProcesses": [],
  "created": "2026-01-15",
  "lastUpdated": "2026-04-30",
  "verified": false,
  "notes": "Corrective rebuild: replaces the generic scaffold with a process-specific step structure and records topology for duplicate detection.",
  "namedCollections": [],
  "graphMetrics": {
    "nodes": 12,
    "edges": 13,
    "conditionals": 2,
    "andGates": 1,
    "orGates": 3,
    "notGates": 0,
    "loops": 1
  },
  "nodeDetails": [
    {
      "id": "N1",
      "label": "Hyperparameter Optimization...",
      "detail": "Hyperparameter Optimization research question",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N2",
      "label": "Model Family",
      "detail": "Model Family",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N3",
      "label": "Metric + Budget",
      "detail": "Metric + Budget",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N4",
      "label": "Search Space",
      "detail": "Search Space",
      "type": "process",
      "role": "Structures & Objects"
    },
    {
      "id": "N5",
      "label": "Sampler Strategy",
      "detail": "Sampler Strategy",
      "type": "process",
      "role": "Structures & Objects"
    },
    {
      "id": "N6",
      "label": "Propose Params",
      "detail": "Propose Params",
      "type": "process",
      "role": "Processing & Operations"
    },
    {
      "id": "N7",
      "label": "Train Candidate",
      "detail": "Train Candidate",
      "type": "process",
      "role": "Processing & Operations"
    },
    {
      "id": "N8",
      "label": "Validate Candidate",
      "detail": "Validate Candidate",
      "type": "decision",
      "role": "Processing & Operations"
    },
    {
      "id": "N9",
      "label": "Trial History",
      "detail": "Trial History",
      "type": "process",
      "role": "Intermediates & States"
    },
    {
      "id": "N10",
      "label": "Best Parameters",
      "detail": "Best Parameters",
      "type": "process",
      "role": "Products & Outputs"
    },
    {
      "id": "N11",
      "label": "Source-grounded check: Pattern...",
      "detail": "Source-grounded check: Pattern Recognition and Machine Learning",
      "type": "decision",
      "role": "Structures & Objects"
    },
    {
      "id": "N12",
      "label": "Hyperparameter Optimization...",
      "detail": "Hyperparameter Optimization prediction/readout",
      "type": "process",
      "role": "Products & Outputs"
    }
  ],
  "flowchartStandard": {
    "name": "source_grounded_rebuild_v1",
    "applied": "2026-04-30",
    "curationStatus": "source_grounded_draft",
    "basis": "cs_exact_template",
    "topologySignature": "55e6f398992186ab",
    "sourceGrounding": "Graph steps are derived from the process title, existing source metadata, and curated process/subfield templates; citations support the process topic and should be reviewed for node-level claims before marking verified."
  }
}