Building a Decision Tree

About
Privacy
Disclaimer

Graph Chatbot

In course

Sit deserunt exercitation do laboris anim. Consectetur ipsum labore excepteur laboris excepteur officia aute duis irure irure. Aliquip magna ex non officia. Sunt mollit magna laborum laborum culpa duis in est aute anim.

Description

This lecture covers the process of building a decision tree to classify mushrooms as poisonous or not based on given data. It includes determining entropy, selecting the root attribute, calculating information gain, and classifying new mushrooms. The lecture also discusses the greedy character of maximizing information gain and decision tree pruning techniques.

Instructor

sunt ullamco enim

Est tempor quis exercitation culpa occaecat ullamco id nostrud irure Lorem. Reprehenderit culpa officia nulla sunt ullamco exercitation aliqua excepteur laboris. Eu eu dolor officia elit ea in laborum ullamco adipisicing ad sint tempor.

Official source

Related lectures (32)

https://mediaspace.epfl.ch/media/0_3im3ao35

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

In course

DEMO: aliquip id adipisicing

Instructor

sunt ullamco enim

Related lectures (32)

Decision Trees: Classification

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

Supervised Learning: Decision Trees

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

Decision Tree Classification

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

Introduction to Data Science

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

Supervised Learning: Classification Algorithms

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