{"id":2302,"date":"2025-06-11T10:16:15","date_gmt":"2025-06-11T04:46:15","guid":{"rendered":"https:\/\/texpertssolutions.com\/notes\/?p=2302"},"modified":"2026-10-21T11:49:28","modified_gmt":"2026-10-21T06:19:28","slug":"how-to-find-my-ml-model-is-overfitted","status":"publish","type":"post","link":"https:\/\/texpertssolutions.com\/notes\/2025\/06\/11\/how-to-find-my-ml-model-is-overfitted\/","title":{"rendered":"How to Detect Overfitting in Machine Learning Models"},"content":{"rendered":"\n

\ud83d\udd0d What is Overfitting?<\/h3>\n\n\n\n

Overfitting is when your language model (LM)<\/strong> is too good at remembering the training data but bad at generalizing<\/strong> to new, unseen data \ud83d\ude2c.<\/p>\n\n\n\n

Think of it like this:<\/p>\n\n\n\n

\ud83d\udcd8 Training Data<\/strong> = Your school textbook
\ud83e\udde0 Overfitted Model<\/strong> = A student who memorized every page but struggles with test questions that are worded differently.<\/p>\n\n\n\n

\n\n\n\n

\ud83d\udea8 Signs Your LM is Overfitted<\/h3>\n\n\n\n

1. \ud83d\udcc9 Low Training Loss, \ud83d\ude80 High Validation\/Test Loss<\/h4>\n\n\n\n
    \n
  • Your model is doing GREAT on the training set \ud83d\ude0e but performs poorly on validation\/test data \ud83d\ude15.<\/li>\n<\/ul>\n\n\n\n

    \u2705 Training loss: Low<\/strong>
    \u274c Validation\/Test loss: High<\/strong><\/p>\n\n\n\n

    \n\n\n\n

    2. \ud83e\ude9e Huge Gap Between Accuracy or Perplexity<\/h4>\n\n\n\n

    If you’re tracking accuracy or perplexity<\/strong> (a measure for language models):<\/p>\n\n\n\n

      \n
    • Accuracy on training = 90%+ \ud83c\udfaf<\/li>\n\n\n\n
    • Accuracy on validation = 50%-60% \ud83d\ude10<\/li>\n<\/ul>\n\n\n\n

      That\u2019s a big red flag \ud83d\udea9<\/p>\n\n\n\n

      \n\n\n\n

      3. \ud83d\udcca Your Loss Curve Looks Like This:<\/h4>\n\n\n\n
        \n
      • Training loss keeps going down<\/strong> \ud83d\udcc9<\/li>\n\n\n\n
      • Validation loss goes down<\/strong> first, then goes back up<\/strong> \ud83d\udcc8<\/li>\n<\/ul>\n\n\n\n

        That’s a classic overfitting curve \ud83e\udde0\ud83d\udd25<\/p>\n\n\n\n

        \n\n\n\n

        \ud83d\udee0\ufe0f How to Fix It?<\/h3>\n\n\n\n

        \u2705 Use more data<\/strong>
        \u2705 Regularization techniques<\/strong> like dropout \ud83d\udd73\ufe0f
        \u2705 Early stopping<\/strong> \u23f9\ufe0f
        \u2705 Smaller model<\/strong> if data is limited
        \u2705 Data Augmentation<\/strong> (e.g., paraphrasing for text)<\/p>\n\n\n\n

        \n\n\n\n

        <\/p>\n","protected":false},"excerpt":{"rendered":"

        \ud83d\udd0d What is Overfitting? Overfitting is when your language model (LM) is too good at remembering the training data but bad at generalizing to new, unseen data \ud83d\ude2c. Think of it like this: \ud83d\udcd8 Training Data = Your school textbook\ud83e\udde0 Overfitted Model = A student who memorized every page but struggles with test questions that…<\/p>\n","protected":false},"author":1,"featured_media":2358,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[641],"tags":[],"class_list":["post-2302","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-machine-learning"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t