Gradient boosted decision trees are a powerful machine learning technique that utilizes an ensemble of weak learners, typically decision trees, to enhance model accuracy by sequentially correcting errors from previous models. This method has gained popularity in machine learning competitions on platforms like Kaggle due to its superior performance. Unlike bagging techniques such as Random Forests, where models are fitted in parallel, gradient boosting builds models sequentially, optimizing the loss function through gradient descent. The article explores various boosting algorithms including AdaBoost, XGBoost, LightGBM, and CatBoost, discussing their implementation for classification and regression tasks using libraries like Scikit-learn, XGBoost, LightGBM, and CatBoost. Each algorithm offers unique features, such as XGBoost's support for parallel computations and LightGBM's leaf-wise growth strategy. While gradient boosting trees generally offer high accuracy and support for categorical features, they can be prone to overfitting and may require significant computational resources. The discussion also highlights the advantages and challenges of using these algorithms, providing insights into their practical application for improving machine learning models.