Descubre el poder de los árboles de decisión en el aprendizaje automático para la clasificación, la regresión y las aplicaciones del mundo real como la sanidad y las finanzas.
A Decision Tree is a versatile and widely used machine learning (ML) algorithm that falls under the category of supervised learning. It uses a tree-like structure to model decisions and their possible consequences, similar to a flowchart. Each internal node represents a test on an attribute (or feature), each branch represents the outcome of the test, and each leaf node represents a class label (in classification tasks) or a continuous value (in regression tasks). Due to their intuitive structure, decision trees are known for being relatively easy to understand and interpret, making them valuable for explainable AI (XAI).
The core idea is to split the dataset into smaller and smaller subsets based on the values of input features, creating a tree structure. The process starts at the root node, which represents the entire dataset. At each node, the algorithm selects the best feature and threshold to split the data in a way that increases the purity or homogeneity of the resulting subsets with respect to the target variable. Common criteria for finding the best split include Gini impurity and information gain (based on entropy), which measure the disorder or randomness in a set. This splitting process continues recursively until a stopping criterion is met, such as reaching a maximum depth, having a minimum number of samples in a node, or achieving pure leaf nodes (nodes containing samples of only one class). To make a prediction for a new data point, it traverses the tree from the root down to a leaf node based on the outcomes of the feature tests, and the prediction is the majority class or average value in that leaf. Careful data preprocessing and feature engineering can significantly impact a decision tree's performance.
Decision trees can be broadly categorized into two main types:
Decision trees offer several benefits:
Sin embargo, también tienen inconvenientes:
Decision trees are employed in various domains:
Decision trees form the basis for more complex ensemble methods like Random Forests and Gradient Boosted Trees (like XGBoost or LightGBM). Random Forests, for instance, build multiple decision trees on different subsets of data and features and aggregate their predictions, often leading to better accuracy and robustness against overfitting compared to a single tree. While powerful for many tabular data problems, decision trees differ significantly from models like Convolutional Neural Networks (CNNs) or Vision Transformers (ViT) used in computer vision. Models like Ultralytics YOLO11 leverage deep learning architectures optimized for tasks like object detection, image classification, and instance segmentation, which involve processing complex, high-dimensional data like images, a domain where single decision trees are less effective. Understanding foundational models like decision trees provides valuable context within the broader landscape of AI and predictive modeling. Tools like Scikit-learn provide popular implementations for decision trees, while platforms like Ultralytics HUB streamline the development and deployment of advanced vision models.