Decision Forests: Structure and Training

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Related lectures (32)

Page 1 of 4

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

Building a Decision Tree

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

Introduction to Data Science

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

Decision Trees: Induction & Attributes

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

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Structured Classifications: Decision Trees and Boosting

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

Decision Tree Classification

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

Supervised Learning: Classification Algorithms

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

Decision Trees: Overfitting and Randomization

Explores decision trees, overfitting, and randomization in supervised learning, emphasizing the importance of managing variance and feature selection.

Supervised Learning: Decision Trees

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