{"id":71,"date":"2026-04-03T11:14:15","date_gmt":"2026-04-03T11:14:15","guid":{"rendered":"https:\/\/gigz.pk\/ml\/?post_type=lesson&p=71"},"modified":"2026-04-07T10:45:32","modified_gmt":"2026-04-07T10:45:32","slug":"gradient-boosting","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/ml\/lesson\/gradient-boosting\/","title":{"rendered":"Gradient Boosting"},"content":{"rendered":"\n

Gradient Boosting is a powerful ensemble Machine Learning algorithm<\/strong> used for both classification and regression tasks. It builds models sequentially, where each new model tries to correct the errors made by the previous models, resulting in high predictive accuracy.<\/p>\n\n\n\n

How Gradient Boosting Works<\/h2>\n\n\n\n
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  1. Start with a Base Model:<\/strong> A simple model, often a decision tree, is trained on the dataset.<\/li>\n\n\n\n
  2. Calculate Errors:<\/strong> The differences between predicted values and actual values (residuals) are computed.<\/li>\n\n\n\n
  3. Train Next Model on Errors:<\/strong> A new model is trained to predict the residuals or errors of the previous model.<\/li>\n\n\n\n
  4. Update Predictions:<\/strong> The predictions of the new model are added to the previous predictions to improve overall accuracy.<\/li>\n\n\n\n
  5. Repeat:<\/strong> Steps 2\u20134 are repeated for a set number of iterations or until improvements stop.<\/li>\n<\/ol>\n\n\n\n

    This sequential approach allows Gradient Boosting to focus on difficult cases that previous models got wrong.<\/p>\n\n\n\n

    Advantages of Gradient Boosting<\/h2>\n\n\n\n