Decision Trees: Overfitting and Randomization

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

Supervised Learning: Classification Algorithms

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

Supervised Learning: Decision Trees

Covers supervised learning with decision trees and feature selection for classification.

Building a Decision Tree

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

Image Classification: Decision Trees & Random Forests

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

Decision Trees and Random Forests: Concepts and Applications

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

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Decision Trees: Induction & Attributes

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

Regression Trees and Ensemble Methods in Machine Learning

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

Addressing Overfitting in Decision Trees

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