What is Tree -based Algorithms? Tree -based algorithms are a class of supervised machine learning models that construct decision trees to typically partition the feature space into regions, enabling a hierarchical representation of complex relationships between input variables and output labels. Decision trees are widely used machine learning algorithms and can be applied to both classification and regression tasks. These models work by splitting data into subsets based on features this process is known as decision making. Each leaf node provides a prediction and the splits create a tree -like structure. A decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root node, branches, internal nodes and leaf nodes. It It works like a flowchart help to make decisions step by step where: Internal nodes represent attribute tests Branches represent attribute values Leaf nodes represent final decisions or predictions. Decision trees are widely used due to their interpretability, flexibility and low ... Decision Tree is a robust machine learning algorithm that also serves as the building block for other widely used and complicated machine learning algorithms like Random Forest, XGBoost, AdaBoost and LightGBM. You can imagine why it’s essential to learn about this topic!
