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Learn about decision tree learning, a supervised learning approach used in statistics, data mining and machine learning. Find out how decision trees are built, used and extended for classification and regression problems.
Pruning is a technique to reduce the size and complexity of decision trees by removing non-critical and redundant nodes. It improves predictive accuracy by reducing overfitting and can be done before or after tree induction.
A decision tree is a hierarchical model that represents decisions and their consequences, used in decision analysis and machine learning. Learn how to draw, analyze, and optimize decision trees, and see examples from business, health, and public health domains.
Random forest is a machine learning technique that constructs multiple decision trees from random subsets of the training data and averages their predictions. It reduces the variance of the model and improves its accuracy, but also increases the bias and loses some interpretability.
ID3 is an algorithm that generates a decision tree from a dataset by iteratively selecting the attribute with the smallest entropy or largest information gain. It is used in machine learning and natural language processing domains and has some properties and limitations.
C4.5 is a decision tree algorithm for classification, based on information entropy and normalized information gain. It is an extension of ID3 and has several improvements and variants, such as C5.0 and See5.
Inductive bias is the set of assumptions that a learning algorithm uses to predict outputs of given inputs. Learn about different types of inductive bias in machine learning, such as maximum margin, minimum cross-validation error, and nearest neighbors.
Machine learning (ML) is a field of artificial intelligence that develops and studies algorithms that can learn from data and generalize to unseen data. ML has many applications in various fields, such as natural language processing, computer vision, and medicine, and is related to data mining, statistics, and neural networks.