computer-science-processes-database/processes/software_engineering/software_engineering-machine-learning.json

{
  "id": "software_engineering-machine-learning",
  "name": "Machine Learning",
  "category": "computer_science",
  "subcategory": "software_engineering",
  "subcategory_name": "Software Engineering",
  "description": "Machine Learning process visualization. This process flowchart outlines key steps, checks, and outputs.",
  "complexity": {
    "nodes": 12,
    "edges": 12,
    "conditionals": 3,
    "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"
    }
  },
  "mermaid": "graph TD\n    N1[\"Machine Learning research question\"]\n    N2[\"Data + Labels\"]\n    N3[\"Objective Metric\"]\n    N4[\"Feature Pipeline\"]\n    N5[\"Model Family\"]\n    N6[\"Train\"]\n    N7{\"Validate\"}\n    N8[\"Deploy\"]\n    N9{\"Monitor\"}\n    N10[\"Production Model\"]\n    N11{\"Source-grounded check: Machine...\"}\n    N12[\"Machine Learning...\"]\n\n    N1 --> N2\n    N2 --> N3\n    N3 --> N4\n    N4 --> N5\n    N5 --> N6\n    N6 --> N7\n    N7 -->|yes| N8\n    N8 --> N9\n    N9 -->|yes| N10\n    N10 --> N11\n    N11 -->|yes| N12\n    N8 -->|iterate| N3\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": "Machine learning with sklearn",
      "authors": "Trappenberg, Thomas P.",
      "journal": "Fundamentals of Machine Learning",
      "year": "2019",
      "pubmed": null,
      "doi": "10.1093/oso/9780198828044.003.0003",
      "url": "https://doi.org/10.1093/oso/9780198828044.003.0003"
    },
    {
      "title": "Software Engineering",
      "authors": "Sommerville, I.",
      "journal": "Pearson",
      "year": "2016",
      "pubmed": null,
      "doi": null,
      "url": "https://www.pearson.com/en-us/subject-catalog/p/software-engineering/P200000003559"
    },
    {
      "title": "Continuous Delivery",
      "authors": "Humble, J.; Farley, D.",
      "journal": "Addison-Wesley",
      "year": "2010",
      "pubmed": null,
      "doi": null,
      "url": "https://continuousdelivery.com/"
    },
    {
      "title": "The Mythical Man-Month",
      "authors": "Brooks, F. P.",
      "journal": "Addison-Wesley",
      "year": "1995",
      "pubmed": null,
      "doi": null,
      "url": "https://www.pearson.com/en-us/subject-catalog/p/mythical-man-month-the-essays-on-software-engineering-anniversary-edition/P200000009016"
    }
  ],
  "keywords": [
    "machine",
    "learning"
  ],
  "relatedProcesses": [],
  "created": "2026-01-08",
  "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.",
  "nodeDetails": [
    {
      "id": "N1",
      "label": "Machine Learning research question",
      "detail": "Machine Learning research question",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N2",
      "label": "Data + Labels",
      "detail": "Data + Labels",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N3",
      "label": "Objective Metric",
      "detail": "Objective Metric",
      "type": "process",
      "role": "Triggers & Inputs"
    },
    {
      "id": "N4",
      "label": "Feature Pipeline",
      "detail": "Feature Pipeline",
      "type": "process",
      "role": "Structures & Objects"
    },
    {
      "id": "N5",
      "label": "Model Family",
      "detail": "Model Family",
      "type": "process",
      "role": "Structures & Objects"
    },
    {
      "id": "N6",
      "label": "Train",
      "detail": "Train",
      "type": "process",
      "role": "Processing & Operations"
    },
    {
      "id": "N7",
      "label": "Validate",
      "detail": "Validate",
      "type": "decision",
      "role": "Processing & Operations"
    },
    {
      "id": "N8",
      "label": "Deploy",
      "detail": "Deploy",
      "type": "process",
      "role": "Processing & Operations"
    },
    {
      "id": "N9",
      "label": "Monitor",
      "detail": "Monitor",
      "type": "decision",
      "role": "Intermediates & States"
    },
    {
      "id": "N10",
      "label": "Production Model",
      "detail": "Production Model",
      "type": "process",
      "role": "Products & Outputs"
    },
    {
      "id": "N11",
      "label": "Source-grounded check: Machine...",
      "detail": "Source-grounded check: Machine learning with sklearn",
      "type": "decision",
      "role": "Structures & Objects"
    },
    {
      "id": "N12",
      "label": "Machine Learning...",
      "detail": "Machine Learning 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": "5ff919f8922b139e",
    "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."
  },
  "namedCollections": [],
  "graphMetrics": {
    "nodes": 12,
    "edges": 12,
    "conditionals": 3,
    "andGates": 1,
    "orGates": 3,
    "notGates": 0,
    "loops": 1
  }
}