Decision Trees: Classification

Related lectures (31)

Page 1 of 4

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

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

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

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers building decision trees to classify mushrooms as poisonous or not.

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

Explores decision trees, overfitting elimination, boosting techniques, and their practical applications in predictive modeling.