What are Decision Trees?
Decision trees are a type of machine learning algorithm used for both classification and regression tasks in the field of artificial intelligence. They represent a model in a tree-like structure where each internal node signifies a feature (or attribute), each branch corresponds to a decision rule, and each leaf node represents an outcome (or class label).
Structure of Decision Trees
The tree begins with a root node, which is split into branches based on the values of the features. The splits are performed using criteria such as Gini impurity or Information Gain. This process continues recursively, resulting in a series of branching decisions that ultimately lead to a prediction.
Application in Computer Vision
In computer vision, decision trees can be employed for image classification and object detection. For example, they can help classify images based on various attributes such as color, texture, and shape. By analyzing these features, the model can accurately categorize images into predefined labels.
Advantages and Limitations
One of the key advantages of decision trees is their interpretability; users can easily understand how decisions are made. However, they can be prone to overfitting, especially with complex datasets. To mitigate this, techniques like pruning or ensemble methods (e.g., Random Forest) are often used.