Lectures related to Decision tree pruning

Decision Trees and Boosting

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

Decision Trees: Induction and Pruning

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

Building a Decision Tree

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

Decision Trees: Induction & Attributes

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

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Ensemble Methods: Random Forest

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

Kinetic Isotope Effects and Linear Free Energy Relationships

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

Machine Learning Basics: Supervised Learning

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

Boosting: Adaboost Algorithm

Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.

Deliberative Agents: Planning and Strategies

Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.

Decision and regression trees

Explores decision and regression trees, impurity measures, learning algorithms, and implementations, including conditional inference trees and tree pruning.

Bagging and Random Forests

Covers ensembling, bagging, random forests, variable importance, and OOB cross-validation in machine learning.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Modular Robots: Self-Reconfigurable Systems

Delves into self-reconfigurable modular robots, discussing advantages, design concepts, planning strategies, and challenges in motion planning and search algorithms.

Data Perturbation: Protecting Privacy in Database Systems

Explores data perturbation for privacy protection in database systems, including aggregate reconstruction and its application in data mining.

Structured Classifications: Decision Trees and Boosting

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

Decision Trees: Classification Log in to Mediaspace to watch this video

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

Regression Models: Performance and Evaluation Log in to Mediaspace to watch this video

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Supervised Learning in Financial Econometrics Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Decision tree pruning

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