Alternating decision tree

IAML7.14 Random forest algorithm

From playlist Decision Tree Learning

Decision Tree 7: continuous, multi-class, regression

Full lecture: http://bit.ly/D-Tree Decision trees are interpretable, they can handle real-valued attributes (by finding appropriate thresholds), and handle multi-class classification and regression with minimal changes.

From playlist Decision Tree

Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1

The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit

From playlist Introduction to Machine Learning 101

IAML7.15 Summary

From playlist Decision Tree Learning

Decision Tree 8: Random Forests

Full lecture: http://bit.ly/D-Tree Decision trees are compact and extremely fast at testing time. They can also handle missing values, and irrelevant attributes naturally. On the downside, they are restricted to axis-aligned splits of the data, and the algorithm is not guaranteed to find

From playlist Decision Tree

Decision trees - A friendly introduction

A video about decision trees, and how to train them on a simple example. Accompanying blog post: https://medium.com/@luis.serrano/splitting-data-by-asking-questions-decision-trees-74afed9cd849 Helper videos: - Gini index: https://www.youtube.com/watch?v=u4IxOk2ijSs - Entropy and informat

From playlist Supervised Learning

Decision Trees (1)

Decision tree basics

From playlist cs273a

How Decision Trees Work

Decision trees are powerful and surprisingly straightforward. Here's how they are grown. Code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py Slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing PERM

From playlist Data Science

(ML 2.1) Classification trees (CART)

Basic intro to decision trees for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

01 Decision analysis as a science

Introduction to decision making under uncertainty

From playlist QUSS GS 260

The Computer Chronicles - Decision Support Software (1988)

Special thanks to archive.org for hosting these episodes. Downloads of all these episodes and more can be found at: http://archive.org/details/computerchronicles

From playlist Computer Chronicles Episodes on Software

Value of Information in the Earth Sciences

Overview, narrated by Tapan Mukerji Eidsvik, J., Mukerji, T. and Bhattacharjya, D., 2015. Value of information in the earth sciences: Integrating spatial modeling and decision analysis. Cambridge University Press.

From playlist Uncertainty Quantification

An Improved Exponential-Time Approximation Algorithm for Fully-Alternating Games... - Andrew Drucker

Computer Science/Discrete Mathematics Seminar I Topic: An Improved Exponential-Time Approximation Algorithm for Fully-Alternating Games Against Nature Speaker: Andrew Drucker Affiliation: University of Chicago Date: January 25, 2021 For more video please visit http://video.ias.edu

From playlist Mathematics

Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess (Paper Explained)

#ai #chess #alphazero Chess is a very old game and both its rules and theory have evolved over thousands of years in the collective effort of millions of humans. Therefore, it is almost impossible to predict the effect of even minor changes to the game rules, because this collective proce

From playlist Reinforcement Learning

Classification Trees in Python from Start to Finish

NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: https://statquest.gumroad.com/l/tzxoh This webinar was recorded 20200528 at 11:00am (New York time). NOTE: This StatQuest assumes are already familiar with: Decision Trees: https:/

From playlist CART - Classification And Regression Trees

StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data

This is just a short follow up to last week's StatQuest where we introduced decision trees. Here we show how decision trees deal with variables that don't improve the tree (feature selection) and how they deal with missing data. For a complete index of all the StatQuest videos, check out:

From playlist StatQuest

13 Decision Making

Recorded: Spring 2014 Lecturer: Dr. Erin M. Buchanan Materials: created for Memory and Cognition (PSY 422) using Smith and Kosslyn (2006) Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/other-courses/

From playlist PSY 422 Memory and Cognition with Dr. B

CSE 519 -- Lecture 25, Fall 2020

From playlist CSE 519 -- Fall 2020

Lecture 9c - Decision Trees

Lecture 9b - Decision Trees

From playlist Graph Theory

A Gentle Introduction to Machine Learning

Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the most basic ideas that make a foundation for the whole thing. These ideas are simple and easy to understand. After watching this StatQues

From playlist StatQuest