Gradient Boosting Vs Decision Tree

Gradient Boosting is an ensemble machine learning technique that builds models in a sequential manner. It combines multiple weak learners, typically decision trees, by adding them one at a time, where each new tree attempts to correct the errors made by the previous ones. The process involves minimizing a loss function through a gradient descent algorithm, allowing the model to improve iteratively. This method is particularly powerful for both classification and regression tasks, as it can capture complex patterns in data while maintaining a balance between bias and variance.

A Decision Tree is a flowchart-like structure used for both classification and regression tasks in machine learning. It consists of nodes that represent features, branches that represent decision rules, and leaves that represent outcomes or final predictions. The tree is constructed by recursively splitting the dataset based on the feature that provides the maximum information gain or minimizes impurity. Decision trees are intuitive and easy to interpret, making them a popular choice for exploratory data analysis, although they can be prone to overfitting if not properly pruned.