The decision tree algorithm stands as a robust tool in the realm of machine learning, finding extensive application in both classification and regression tasks within supervised learning. This method excels in predicting outcomes for new data points by discerning patterns from the training data.
In the context of classification, a decision tree takes the form of a graphical representation depicting a set of rules instrumental in categorizing data into distinct classes. Its structure mirrors that of a tree, with internal nodes representing features or attributes and leaf nodes signifying the ultimate outcome or class label.
The branches of the tree articulate the decision rules governing the division of data into subsets based on feature values. The principal objective of the decision tree is to formulate a model capable of accurately predicting the class label for a given data point. This is achieved through a sequence of steps: selecting the optimal feature to bifurcate the data, constructing the tree framework, and assigning class labels to the leaf nodes.
Initiating at the root node, the algorithm identifies the feature that most effectively divides the data into subsets. The choice of feature hinges on diverse criteria such as Gini impurity and information gain. Once a feature is chosen, the data undergoes division into subsets according to specified conditions, with each branch representing a potential outcome aligned with the decision rule associated with the selected feature.
The recursive application of this process to each data subset persists until a stopping condition is met, be it reaching a maximum depth or a minimum number of samples in a leaf node. Upon the completion of tree construction, each leaf node aligns with a specific class label. When presented with new data, the decision tree traverses based on the feature values of the data, culminating in the assignment of the final prediction as the class label affiliated with the reached leaf node