Clasificación and Regresión Trees (CART) are powerful aprendizaje automático algorithms that use a tree-like model of decisions to predict outcomes based on input features. The CART methodology can be applied to two main types of modelado predictivo: classification and regression.
In classification, CART is used to categorize data into discrete classes. It works by splitting the dataset into subsets based on the value of input features, creating branches in the tree, until it reaches a prediction or a terminal node. For example, if you were predicting whether an email is spam or not, the tree might first check if the email contains certain keywords, creating branches based on the presence or absence of those keywords.
In regression, CART predicts continuous outcomes. The process is similar; however, at each node, the algorithm determines the best split that minimizes the variance in the continuous outcome variable. For instance, if you were predicting house prices, the tree might first split based on the size of the house, then further refine predictions based on location and other factors.
CART es conocido por su simplicidad y interpretability because the resulting trees can be visualized easily, making it straightforward to understand the decision-making process. However, they can be prone to overfitting, especially with complex datasets. Techniques such as pruning (removing branches that have little importance) can help improve performance.
Overall, CART is widely used in various applications, from finance to healthcare, for tasks such as evaluación de riesgos, customer segmentation, and clinical decision support.