Classification algorithms | Ensemble learning
In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting. When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. "Informally, [the hypothesis boosting] problem asks whether an efficient learning algorithm […] that outputs a hypothesis whose performance is only slightly better than random guessing [i.e. a weak learner] implies the existence of an efficient algorithm that outputs a hypothesis of arbitrary accuracy [i.e. a strong learner]." Algorithms that achieve hypothesis boosting quickly became simply known as "boosting". Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining), as a general technique, is more or less synonymous with boosting. (Wikipedia).
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
Machine Learning using Boosting Regression in JASP free software | Supervised learning
In this video, I will demonstrate how to boosting regression which is a machine learning technique. I discuss fit and output and show how to interpret them. Useful links: Jasp: https://jasp-stats.org/download/ Regression: https://www.youtube.com/watch?v=3sQnO02f8Z0&list=UUfu2GCdjq50W-k
From playlist Machine Learning
In this video, you’ll learn more about the evolution of machine learning and its impact on daily life. Visit https://www.gcflearnfree.org/thenow/what-is-machine-learning/1/ for our text-based lesson. This video includes information on: • How machine learning works • How machine learning i
From playlist Machine Learning
Introduction To Machine Learning | Machine Learning Basics for Beginners | ML Basics | Simplilearn
Machine Learning is a trending topic nowadays. This Introduction to Machine Learning video will help you to understand what is Machine Learning, importance of Machine Learning, advantages and disadvantages of Machine Learning, what are the types of Machine Learning - supervised, unsupervis
Machine Learning with scikit learn Part Two | SciPy 2017 Tutorial | Andreas Mueller & Alexandre Gram
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, fro
From playlist talks
15b Machine Learning: Gradient Boosting
Lecture on ensemble machine learning with boosting with a demonstration based on tree based boosting.
From playlist Machine Learning
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | Simplilearn
This Machine Learning tutorial will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning
Boosting Machine Learning Tutorial | Adaptive Boosting, Gradient Boosting, XGBoost | Edureka
** Machine Learning Certification Training using Python: https://www.edureka.co/python ** This Edureka session will help you understand all about Boosting Machine Learning and boosting algorithms and how they can be implemented to increase the efficiency of Machine Learning models. The fol
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Machine Learning: Zero to Hero
This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will guide you
From playlist Machine Learning
Ensemble Learning | Ensemble Learning In Machine Learning | Machine Learning Tutorial | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=EnsembleLearning&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilear
Intro to XGBoost Models (decision-tree-based ensemble ML algorithms)
Frank Kane, Sundog Education founder and the author of liveVideo course 📼 Machine Learning, Data Science and Deep Learning with Python | http://mng.bz/gggR 📼 takes a deep dive into one of the most powerful machine learning algorithm, eXtreme Gradient Boosting, using a Jupyter notebook with
From playlist Machine Learning
Complete Beginners Guide to XGBoost Models
Frank Kane, Sundog Education founder and the author of liveVideo course 📼 Machine Learning, Data Science and Deep Learning with Python | http://mng.bz/o27M 📼 takes a deep dive into one of the most powerful machine learning algorithm, eXtreme Gradient Boosting, using a Jupyter notebook wit
From playlist Machine Learning
Model Selection and Boosting in Machine Learning - Part 2 | Machine Learning Tutorial | Edureka
🔥E&ICT Academy, NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp This Edureka video on Model Selection and Boosting in Machine Learning (Part 1 - https://youtu.be/fvGyjj1dVPg ) Step by step guide to select and boost your models
From playlist Edureka Live Classes 2020
Code with me (live): How to make your first Kaggle submission from scratch!
Let's explore the Kaggle Titanic data and make a submission together! Thank you to Coursera for sponsoring this video. You can check out the Applied Data Science with Python Specialization I mentioned (and more) here: http://bit.ly/appliedDSwithPython Get the code here: http://bit.ly/you
From playlist Kagglers on YouTube | Kaggle
REFERENCES [1] A Short Introduction to Boosting: https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf [2] A Theory of the Learnable (Valiant, 1984): http://web.mit.edu/6.435/www/Valiant84.pdf. This introduced the PAC Learning model [3] PAC Learning Model: https://www.youtube.com/wa
From playlist Algorithms and Concepts
Gradient Boost Part 1 (of 4): Regression Main Ideas
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous
From playlist StatQuest
Reinforcement Learning Course - Full Machine Learning Tutorial
Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning
From playlist Machine Learning