{"id":12410,"date":"2020-02-19T13:50:50","date_gmt":"2020-02-19T08:20:50","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/overfitting-and-underfitting-in-machine-learning\/"},"modified":"2025-03-26T11:40:58","modified_gmt":"2025-03-26T06:10:58","slug":"overfitting-and-underfitting-in-machine-learning","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/overfitting-and-underfitting-in-machine-learning\/","title":{"rendered":"Overfitting and Underfitting in Machine Learning"},"content":{"rendered":"\n

Are Your Machine Learning Models Really Smart?<\/strong> \ud83e\udd14<\/p>\n\n\n\n

Ever trained a model that performs amazingly on training data but fails miserably on new data? That\u2019s where Overfitting<\/strong> and Underfitting<\/strong> come in! These two problems can make or break your machine learning model<\/a>\u2019s ability to generalize.<\/p>\n\n\n\n

In this article, we\u2019ll break down these concepts, understand why they happen, and explore effective techniques to fix them. Let\u2019s dive in!<\/p>\n\n\n\n

Understanding Model Performance: Overfitting vs. Underfitting<\/strong><\/h2>\n\n\n\n

Whenever we train a machine learning model, we aim to create a system that can accurately predict outcomes for new, unseen data. To evaluate a model\u2019s performance, we typically split our dataset into:<\/p>\n\n\n\n