Building a Decision Tree

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Related lectures (32)

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Decision Forests: Structure and Training

Covers decision forests, training, weak learners, entropy, boosting, 3D pose estimation, and practical applications.

Information Measures: Entropy and Information Theory

Explains how entropy measures uncertainty in a system based on possible outcomes.

Random Walks and Moran Model in Population Genetics

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

Information Measures

Covers information measures like entropy and Kullback-Leibler divergence.

Mutual Information: Continued

Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Decision Trees: Induction and Pruning

Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.

Decision Trees: Induction & Attributes

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Mutual Information and Entropy

Explores mutual information and entropy calculation between random variables.

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