Building a Decision Tree

Related lectures (32)

Page 3 of 4

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Covers information measures, entropy, mutual information, and data processing inequality in signal representation.

Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Explains probability distribution, entropy, and Gibbs free entropy, along with the Weiss model.

Reviews heat transfer mechanisms, entropy, and thermodynamic potentials in relation to equilibrium and Carnot cycles.

Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.

Discusses conditional entropy and its role in information theory and data compression.