Decision Trees: Induction & Attributes

Related lectures (32)

Page 3 of 4

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

Explores overfitting in decision trees and introduces random forests as a solution.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.

Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.

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

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.