Classification algorithms | Ensemble learning
AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gรถdel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals on the real line. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. In some problems it can be less susceptible to the overfitting problem than other learning algorithms. The individual learners can be weak, but as long as the performance of each one is slightly better than random guessing, the final model can be proven to converge to a strong learner. Although AdaBoost is typically used to combine weak base learners (such as decision stumps), it has been shown that it can also effectively combine strong base learners (such as deep decision trees), producing an even more accurate model. Every learning algorithm tends to suit some problem types better than others, and typically has many different parameters and configurations to adjust before it achieves optimal performance on a dataset. AdaBoost (with decision trees as the weak learners) is often referred to as the best out-of-the-box classifier. When used with decision tree learning, information gathered at each stage of the AdaBoost algorithm about the relative 'hardness' of each training sample is fed into the tree growing algorithm such that later trees tend to focus on harder-to-classify examples. (Wikipedia).
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5 Best Practices In DevOps Culture | What is DevOps? | Edureka
๐ฅ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐๐ฏ๐๐ฉ๐ฌ ๐๐จ๐ฌ๐ญ ๐๐ซ๐๐๐ฎ๐๐ญ๐ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ ๐ฐ๐ข๐ญ๐ก ๐๐ฎ๐ซ๐๐ฎ๐ ๐๐ง๐ข๐ฏ๐๐ซ๐ฌ๐ข๐ญ๐ฒ: https://www.edureka.co/executive-programs/purdue-devops This tutorial explains what is DevOps. It will help you understand some of its best practices in DevOps culture. This video will also provide an insight into how different
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Clojure - the Reader and Evaluator (4/4)
Part of a series teaching the Clojure language. For other programming topics, visit http://codeschool.org
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AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just a simple twist on decision trees and random forests. NOTE: This video assumes you already know about Decision Trees... https://youtu.be/_L39rN6gz7Y ...and Random Forests
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Machine Learning Lecture 33 "Boosting Continued" -Cornell CS4780 SP17
Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
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
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AdaBoost in Python - Machine Learning From Scratch 13 - Python Tutorial
Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook In this Machine Learning from Scratch Tutorial, we are going to implement the AdaBoost algorithm using only built-in Python modules and numpy. AdaBoost is an ensemble technique that attempts to create a strong classifie
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Ensemble Learning | Ensemble Learning In Machine Learning | Machine Learning Tutorial | Simplilearn
๐ฅArtificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=EnsembleLearning&utm_medium=Descriptionff&utm_source=youtube ๐ฅProfessional Certificate Program In AI And Machine Learning: https://www.simplilear
AdaBoost : Data Science Concepts
How do we put together lots of weak models into a STRONG model?
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Clojure - the Reader and Evaluator (2/4)
Part of a series teaching the Clojure language. For other programming topics, visit http://codeschool.org
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AdaBoost (Adaptive Boosting) ensemble learning technique for classification
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Machine Learning Lecture 34 "Boosting / Adaboost" -Cornell CS4780 SP17
Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
How to Work with Wikipedia Sandbox
This is a short video that helps students or editors of Wikipedia to access and edit in the Sandbox of their user account. This was made for the Wiki Edu Project. I do not own or hold copyright over any aspect of the Wikipedia site or its pages. ***There is no audio***
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