Decision tree is a powerful machine-learning algorithm that is widely used for both classification and regression tasks. It is a supervised learning method that helps to predict the outcome of a new data point based on the patterns learned from the training data. In the context of classification, a decision tree is a graphical representation of a set of rules that helps to classify the data into different categories. It is a tree-like structure where each internal node represents a feature or attribute, and each leaf node represents the outcome or class label.

The branches of the tree represent the decision rules that are used to split the data into subsets based on the values of the features. The primary goal of a decision tree is to create a model that can accurately predict the class label of a new data point. To achieve this, the algorithm follows a series of steps: selecting the best feature to split the data, creating a tree structure, and assigning class labels to the leaf nodes. The process starts at the root node, where the algorithm selects the feature that best splits the data into subsets. The feature selection is based on various criteria, such as Gini impurity and information gain.

Once the feature is selected, the data is split into subsets based on certain conditions, and each branch represents a possible outcome of the decision rule associated with the selected feature. The process is then applied recursively to each subset of data until a stopping condition is met, such as reaching a maximum depth or a minimum number of samples in a leaf node. Once the tree is constructed, each leaf node is associated with a class label, and when new data is presented to the tree, it traverses the tree based on the feature values of the data. The final prediction is the class label associated with the leaf node reached.