Döntésfákban

Döntésfákban, often translated as "decision trees" in English, are a fundamental concept in machine learning and artificial intelligence. They are a type of supervised learning algorithm used for both classification and regression tasks. A decision tree is a flowchart-like structure where each internal node represents a test on an attribute (e.g., whether a feature is greater than a certain value), each branch represents the outcome of the test, and each leaf node represents a class label (in classification) or a continuous value (in regression). The path from the root node to a leaf node represents a series of decisions that leads to a prediction.

The construction of a decision tree typically involves recursively splitting the dataset into subsets based on the values of input attributes. The splitting criterion is chosen to maximize the information gain or minimize impurity at each node. Common impurity measures include Gini impurity and entropy. Algorithms like ID3, C4.5, and CART are widely used for building decision trees.

Decision trees are popular due to their interpretability. The rules learned by the tree can be easily understood by humans, making them valuable for tasks where explaining the decision-making process is important. However, single decision trees can be prone to overfitting, meaning they may perform poorly on unseen data. To address this, ensemble methods like Random Forests and Gradient Boosting, which combine multiple decision trees, are often employed to improve accuracy and robustness. These methods leverage the strengths of decision trees while mitigating their weaknesses.