Data mining and machine learning software

XGBoost

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).

XGBoost
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Stereolab - The Super-It

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How Xavix Works

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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

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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

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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

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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

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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

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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

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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

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XGBoost Better Than Deep Learning for Time Series - Data Scientist Reacts Ep. 46

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From playlist Data Scientist Reacts

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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

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Related pages

LightGBM | Julia (programming language) | Scala (programming language) | OpenCL | Apache Spark | Gradient boosting | Apache Flink | Scikit-learn | Feature selection | R (programming language) | Newton's method in optimization | Newton's method | Randomization | Taylor series | Regularization (mathematics)