Decision tree learning

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Related lectures (32)

Kinetic Isotope Effects and Linear Free Energy Relationships

Explores kinetic isotope effects and Linear Free Energy Relationships, introducing machine learning methods for chemistry applications.

Image Classification: Decision Trees & Random Forests

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

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Decision Trees and Boosting

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

Decision Trees and CLT's: Inference and Machine Learning

Explores decision trees, ensembles, CLT, inference, machine learning, diagnostic methods, boosting, and variance estimation.

Supervised Learning: Classification Algorithms

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

Addressing Overfitting in Decision Trees

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

Apprenticeship in Venice: Garzoni Project

Delves into the apprenticeship system in Venice between the 16th and 18th centuries, focusing on the 'garzoni'.

Ensemble Methods: Random Forest

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

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