Data mining and machine learning software
XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Windows, and macOS. From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask. It has gained much popularity and attention recently as the algorithm of choice for many winning teams of machine learning competitions. (Wikipedia).
Learn all about Xavix through an interview and demo from e3 2006.
From playlist Classic HowStuffWorks
Every year thousands of new electronics enter the marketplace. Watch our video coverage to learn what cool new things HowStuffWorks found at E3 2006.
From playlist Classic HowStuffWorks
Watch a car park itself! Credits: , HowStuffWorks
From playlist Classic HowStuffWorks
From playlist the absolute best of stereolab
From playlist the absolute best of stereolab
XGBoost 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.org/product/jupyter-notebook-xgboost-in-python/ NOTE: This StatQuest assumes that you are already familiar with: XGBoost for Regression: https://youtu.be/OtD8wVaFm
From playlist Machine Learning
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
XGBoost Part 1 (of 4): Regression
XGBoost is an extreme machine learning algorithm, and that means it's got lots of parts. In this video, we focus on the unique regression trees that XGBoost uses when applied to Regression problems. NOTE: This StatQuest assumes that you are already familiar with... The main ideas behind G
From playlist StatQuest
XGBoost Part 3 (of 4): Mathematical Details
In this video we dive into the nitty-gritty details of the math behind XGBoost trees. We derive the equations for the Output Values from the leaves as well as the Similarity Score. Then we show how these general equations are customized for Regression or Classification by their respective
From playlist StatQuest
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
XGBoost Part 2 (of 4): Classification
In this video we pick up where we left off in part 1 and cover how XGBoost trees are built for Classification. NOTE: This StatQuest assumes that you are already familiar with... XGBoost Part 1: XGBoost Trees for Regression: https://youtu.be/OtD8wVaFm6E ...the main ideas behind Gradient B
From playlist StatQuest
XGBoost Part 4 (of 4): Crazy Cool Optimizations
This video covers all kinds of extra optimizations that XGBoost uses when the training dataset is huge. So we'll talk about the Approximate Greedy Algorithm, Parallel Learning, The Weighted Quantile Sketch, Sparsity-Aware Split Finding (i.e. how XGBoost deals with missing data and uses def
From playlist StatQuest
XGBoost Better Than Deep Learning for Time Series - Data Scientist Reacts Ep. 46
Nick Wan is the Director of Analytics for the Cincinnati Reds. He streams data science on Twitch and reacts to the latest news, sports, memes and everything in between. Twitter: https://twitter.com/nickwan WATCH LIVE ON TWITCH: https://twitch.tv/nickwan_datasci https://twitch.tv/nickwan
From playlist Data Scientist Reacts
XGBoost: Regression step by step with Python | Data Analysis | Supervised learning | Real estate
Do you want to learn the different steps of machine learning with eXtreme Gradient Boosting in regression?? In this amazing episode, we'll cover step by step a complete machine learning analysis for regression through the extreme gradient boosting regressor using the PRICE HOUSE EVAL wit
From playlist Python
Get a New Perspective on the world with HowStuffWorks.com
From playlist Classic HowStuffWorks