Decision trees | Classification algorithms | Ensemble learning

Gradient boosting

Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. A gradient-boosted trees model is built in a stage-wise fashion as in other boosting methods, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function. (Wikipedia).

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Gradient Boosting : Data Science's Silver Bullet

A dive into the all-powerful gradient boosting method! My Patreon : https://www.patreon.com/user?u=49277905

From playlist Data Science Concepts

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Ensembles (3): Gradient Boosting

Gradient boosting ensemble technique for regression

From playlist cs273a

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Applied ML 2020 - 08 - Gradient Boosting

Materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/

From playlist Applied Machine Learning 2020

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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

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Gradient Boost Part 2 (of 4): Regression Details

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 second part in a series that walks through it one step at a time. This video focuses on the original Gradient Boost algorithm used to predict a continuou

From playlist StatQuest

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The Gradient

This video explains what information the gradient provides about a given function. http://mathispower4u.wordpress.com/

From playlist Functions of Several Variables - Calculus

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15b Machine Learning: Gradient Boosting

Lecture on ensemble machine learning with boosting with a demonstration based on tree based boosting.

From playlist Machine Learning

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Boosting - EXPLAINED!

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

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Applied Machine Learning 2019 - Lecture 09 - Gradient boosting; Calibration

Gradient boosting and "extreme" gradient boosting Calibration curves and calibrating classifiers with CalibratedClassifierCV. Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

From playlist Applied Machine Learning - Spring 2019

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Gradient descent

This video follows on from the discussion on linear regression as a shallow learner ( https://www.youtube.com/watch?v=cnnCrijAVlc ) and the video on derivatives in deep learning ( https://www.youtube.com/watch?v=wiiPVB9tkBY ). This is a deeper dive into gradient descent and the use of th

From playlist Introduction to deep learning for everyone

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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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KS5 - Sketching the Gradient Function

"Sketch the gradient function for a given curve, e.g. in relation to speed and acceleration."

From playlist Differentiation (AS/Beginner)

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Gradient Boost Part 3 (of 4): Classification

This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This video gives focuses on the main ideas behind this technique. The next video in this series will focus more on the math and how it works with the underlying algorithm. T

From playlist StatQuest

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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"

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Gradient of a function.

Download the free PDF http://tinyurl.com/EngMathYT A basic tutorial on the gradient field of a function. We show how to compute the gradient; its geometric significance; and how it is used when computing the directional derivative. The gradient is a basic property of vector calculus. NOT

From playlist Engineering Mathematics

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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

From playlist 🔥Artificial Intelligence | Artificial Intelligence Course | Updated Artificial Intelligence And Machine Learning Playlist 2023 | Simplilearn

Related pages

Loss function | Random forest | XGBoost | Regression analysis | AdaBoost | Differentiable function | Decision tree learning | Errors and residuals | Interpretability | Learning rate | Sides of an equation | Decision stump | Least squares | Greedy algorithm | Regularization (mathematics) | Interaction (statistics) | Boosting (machine learning) | Decision tree | Overfitting | Ensemble learning | R (programming language) | Gradient descent | LightGBM | Hyperparameter (machine learning) | Line search | Mean squared error | Out-of-bag error