{
  "source_file": "0930",
  "source_platform": "Dev.to",
  "author": "zeromathai",
  "title_listed": "How Classical Machine Learning Works — From Linear Models to Random Forests",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "技术教程",
  "is_paid_flag": "否",
  "url": "https://dev.to/zeromathai/how-classical-machine-learning-works-from-linear-models-to-random-forests-5371",
  "publish_date": "2026-05-13",
  "corpus_stage": "",
  "notes": "",
  "id": "4cd82d57ad7f",
  "collected_at": "2026-10-02T13:50:56",
  "title": "How Classical Machine Learning Works — From Linear Models to Random Forests - DEV Community",
  "body_markdown": "Classical machine learning is not outdated.\nIt is still one of the best ways to understand how models learn from structured data.\nBefore deep learning learns representations automatically, classical ML asks a more explicit question:\nWhat features should we use, and what model should learn from them?\nCore Idea\nClassical machine learning usually starts with structured data.\nRows.\nColumns.\nFeatures.\nLabels.\nThe model learns a relationship between input features and target outputs.\nThis makes classical ML especially useful when interpretability, tabular data, and clear model behavior matter.\nThe Key Structure\nA simple classical ML workflow looks like this:\nData → Features → Model → Prediction → Evaluation → Improvement\nMore compactly:\nClassical ML = feature engineering + model learning + generalization\nThe model does not magically understand raw data.\nIt depends heavily on the quality of the features.\nThat is why feature design is so important.\nImplementation View\nAt a high level, a classical ML workflow looks like this:\ncollect structured data\n\nclean and prepare features\n\nsplit data into train and test sets\n\nchoose a model\n\ntrain the model\n\nevaluate generalization\n\ntune complexity if needed\nEnter fullscreen mode\nExit fullscreen mode\nThis is why classical ML is still practical.\nYou can often inspect the pipeline clearly.\nYou can compare models quickly.\nYou can understand why a model works or fails.\nConcrete Example\nImagine predicting whether a customer will churn.\nYour features might include:\nnumber of logins\nsubscription length\nsupport tickets\nrecent activity\npayment history\nA classical ML model learns how those features relate to churn.\nLogistic Regression may give a simple probability.\nSVM may focus on a cleaner boundary.\nRandom Forest may capture more complex feature interactions.\nSame data.\nDifferent model assumptions.\nLinear Models as the Starting Point\nLinear models are the foundation.\nThey assume the prediction can be built from a weighted combination of features.\nConceptually:\nprediction = weight1 × feature1 + weight2 × feature2 + ... + bias\nThis structure is simple.\nBut it teaches the most important idea:\nEach feature contributes to the prediction.\nThat makes linear models easy to interpret and useful as a baseline.\nLogistic Regression vs SVM vs Random Forest\nThese three models show the classical ML landscape well.\nLogistic Regression:\nsimple classification model\noutputs probability-like scores\neasy to interpret\nstrong baseline for structured data\nSupport Vector Machine:\nfocuses on finding a good decision boundary\nuses margins to separate classes\ncan work well when boundary quality matters\nless transparent than simple linear models\nRandom Forest:\ncombines many decision trees\ncaptures nonlinear feature interactions\nreduces overfitting compared with a single tree\noften strong on tabular data\nSo the practical choice depends on the problem.\nNeed interpretability?\nStart with Logistic Regression.\nNeed a stronger boundary?\nTry SVM.\nNeed nonlinear patterns in tabular data?\nTry Random Forest.\nModel Complexity and Generalization\nA model should not only fit training data.\nIt should work on new data.\nThat is generalization.\nToo simple:\nThe model underfits.\nIt misses important patterns.\nToo complex:\nThe model overfits.\nIt memorizes noise.\nThe goal is not maximum training accuracy.\nThe goal is reliable performance on unseen data.\nThat is why model complexity matters.\nWhere Gradient Descent Fits\nSome models learn parameters through optimization.\nGradient descent is one of the most important optimization methods.\nIt updates parameters in the direction that reduces loss.\nA simple view:\nnew parameter = old parameter - learning rate × gradient\nThe gradient tells the direction.\nThe learning rate controls the step size.\nThis idea connects classical ML directly to modern deep learning.\nThe models may differ.\nBut optimization remains central.\nClassical ML vs Deep Learning\nThis comparison makes the workflow clear.\nClassical Machine Learning:\nworks well with structured data\noften depends on feature engineering\nusually easier to interpret\ncan perform strongly with smaller datasets\nDeep Learning:\nlearns representations automatically\nworks well with images, text, audio, and large-scale data\noften needs more data and compute\ncan be harder to interpret\nSo classical ML is not simply “old ML.”\nIt is the right tool when the data and constraints fit.\nRecommended Learning Order\nIf classical machine learning feels broad, learn it in this order:\nLinear Models\nLogistic Regression\nSupport Vector Machine\nRandom Forest\nModel Complexity and Generalization\nGradient Descent\nThis order works because you first understand the model baseline.\nThen you compare model families.\nThen you connect prediction quality to generalization and optimization.\nTakeaway\nClassical machine learning is built around structured data, features, models, and generalization.\nThe shortest version is:\nClassical ML = features + interpretable models + generalization\nLinear models give the foundation.\nLogistic Regression gives a practical classification start.\nSVM improves the decision-boundary view.\nRandom Forest captures stronger nonlinear patterns.\nIf you remember one idea, remember this:\nClassical machine learning works best when you understand the features, the model assumptions, and how well the model generalizes.\nDiscussion\nWhen starting a new ML problem, do you usually begin with a simple baseline like Logistic Regression, or jump straight to stronger models like Random Forest?\nOriginally published at zeromathai.com.\nOriginal article:\nhttps://zeromathai.com/en/classical-machine-learning-hub-en/\nGitHub Resources\nAI diagrams, study notes, and visual guides:\nhttps://github.com/zeromathai/zeromathai-ai",
  "metadata": {
    "title": "How Classical Machine Learning Works — From Linear Models to Random Forests - DEV Community",
    "description": "Classical machine learning is not outdated.  It is still one of the best ways to understand how... Tagged with machinelearning, ai, datascience, algorithms.",
    "keywords": "machinelearning, ai, datascience, algorithms, software, coding, development, engineering, inclusive, community",
    "author": null,
    "og:type": "article",
    "og:url": "https://dev.to/zeromathai/how-classical-machine-learning-works-from-linear-models-to-random-forests-5371",
    "og:title": "How Classical Machine Learning Works — From Linear Models to Random Forests",
    "og:description": "Classical machine learning is not outdated.  It is still one of the best ways to understand how...",
    "og:site_name": "DEV Community",
    "og:image": "https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fv1x6cvot4forqx08aiwg.png",
    "twitter:site": "@thepracticaldev",
    "twitter:creator": "@",
    "twitter:title": "How Classical Machine Learning Works — From Linear Models to Random Forests",
    "twitter:description": "Classical machine learning is not outdated.  It is still one of the best ways to understand how...",
    "twitter:card": "summary_large_image",
    "twitter:widgets:new-embed-design": "on",
    "twitter:image:src": "https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fv1x6cvot4forqx08aiwg.png"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 200,
    "error": ""
  },
  "code_blocks": [
    {
      "file": "attachments/4cd82d57ad7f/code/body-000.txt",
      "chars": 174,
      "language": "text"
    }
  ],
  "images": [],
  "audio": [],
  "videos": [],
  "links": [
    {
      "href": "https://zeromathai.com/en/classical-machine-learning-hub-en/",
      "label": "https://zeromathai.com/en/classical-machine-learning-hub-en/",
      "type": "a"
    },
    {
      "href": "https://github.com/zeromathai/zeromathai-ai",
      "label": "https://github.com/zeromathai/zeromathai-ai",
      "type": "a"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-14T10:48:21Z",
  "source_url": "https://dev.to/zeromathai/how-classical-machine-learning-works-from-linear-models-to-random-forests-5371",
  "papers": [],
  "actual_video_files": [],
  "body_chars": 5743,
  "expected_body_images": 0,
  "discovery_status": "ok",
  "delivery_status": "ok",
  "missing_reason": "",
  "raw_html": "attachments/4cd82d57ad7f/pages/source.html",
  "local_html": "attachments/4cd82d57ad7f/pages/article.html",
  "body_base_path": "attachments/4cd82d57ad7f/pages/",
  "body_markdown_path": "attachments/4cd82d57ad7f/pages/article.md"
}