Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: [email protected]
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Unlock the full potential of AI with Building LLMs for Productionβ€”our 470+ page guide to mastering LLMs with practical projects and expert insights!

Publication

AdaBoost Explained From Its Original Paper
Artificial Intelligence   Latest   Machine Learning

AdaBoost Explained From Its Original Paper

Last Updated on July 5, 2024 by Editorial Team

Author(s): Christian Guerra

Originally published on Towards AI.

This publication is meant to show a very popular ML algorithm in complete detail, how it works, the math behind it, how to execute it in Python and an explanation of the proofs of the original paper. There will be math and code, but it is written in a way that allows you to decide which are the fun parts.

A bit on the origins of the algorithm: It was proposed by Yoav Freund and Robert E. Schapire in a 1997 paper, β€œA Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”—a beautiful and brilliant publication for an effective and useful algorithm.

Let’s start with the pros, cons, and uses of AdaBoost.

Advantages: improves performance and achieves higher accuracy than a single model. It reduces overfitting compared to some other machine learning algorithms.

Disadvantages: AdaBoost can be sensitive to noisy data and outliers. It requires careful tuning, and the performance can depend on the choice of weak learners and the number of iterations. It cannot be parallelized (or only partially), since each predictor can only be trained after the previous predictor has been trained and evaluated. As a result, it does not scale as well as bagging or pasting.

Applications: image recognition, text classification,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming aΒ sponsor.

Published via Towards AI

Feedback ↓