Quantile regression averaging

Linear regression

Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.

From playlist Learning medical statistics with python and Jupyter notebooks

Quantile Regression - EXPLAINED!

Quantile regression - Hope the explanation wasn't too all over the place Follow me on M E D I U M: https://towardsdatascience.com/likelihood-probability-and-the-math-you-should-know-9bf66db5241b CODE: https://github.com/ajhalthor/quantile-regression

From playlist Code Machine Learning

Ex: Comparing Linear and Exponential Regression

This video provides an example on how to perform linear regression and exponential regression on the TI84. The best model is identified based up the value of R^2. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com

From playlist Solving Applications Using Exponential Equations / Compounded and Continuous Interest / Exponential Regression

Introduction to Regression Analysis

This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.

From playlist Performing Linear Regression and Correlation

Inverse normal with Z Table

Determining values of a variable at a particular percentile in a normal distribution

From playlist Unit 2: Normal Distributions

Intro to Regression Analysis

Overview of regression analysis, linear and multiple regression, and the coefficient of determination.

From playlist Regression Analysis

An Introduction to Linear Regression Analysis

Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon

From playlist Linear Regression.

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

Tilmann Gneiting: Isotonic Distributional Regression (IDR) - Leveraging Monotonicity, Uniquely So!

CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 02, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians

From playlist Virtual Conference

Assumption-free prediction intervals for black-box regression algorithms - Aaditya Ramdas

Seminar on Theoretical Machine Learning Topic: Assumption-free prediction intervals for black-box regression algorithms Speaker: Aaditya Ramdas Affiliation: Carnegie Mellon University Date: April 21, 2020 For more video please visit http://video.ias.edu

From playlist Mathematics

RegressionInferences.2.DistributionsBetaHat

This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources

From playlist Inferences about Regression

Jing Zhou

From playlist Contributed talks One World Symposium 2020

Logistic Regression Details Pt1: Coefficients

When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odds ratio), but, to be honest, the easiest way to make sense of these are through examples. In this StatQuest, I walk you though two Logistic Regression Examples, step-by-s

From playlist StatQuest

Data Science - Part IV - Regression Analysis and ANOVA Concepts

For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-leve

From playlist Data Science

Risk and robustness in RL: Nothing ventured, nothing gained - Shie Mannor

Workshop on New Directions in Reinforcement Learning and Control Topic: Risk and robustness in RL: Nothing ventured, nothing gained Speaker: Shie Mannor Affiliation: Technion Date: November 8, 2019 For more video please visit http://video.ias.edu

From playlist Mathematics

Emmanuel Candes

From playlist Plenary talks One World Symposium 2020

Conrad Wasko - Changes in rainfall and flooding across Australia

Dr. Conrad Wasko (University of Melbourne) presents "Changes in rainfall and flooding across Australia", 24 June 2022.

From playlist Statistics Across Campuses

Recent progress in predictive inference - Emmanuel Candes, Stanford University

Emmanuel Candes - Stanford University Machine learning algorithms provide predictions with a self-reported confidence score, but they are frequently inaccurate and uncalibrated, limiting their use in sensitive applications. This talk introduces novel calibration techniques addressing two

From playlist Interpretability, safety, and security in AI

Simplified Machine Learning Workflows with Anton Antonov, Session #3: Quantile Regression (Part 3)

Anton Antonov presents the first session on quantile regression workflows in Wolfram Language.

From playlist Simplified Machine Learning Workflows with Anton Antonov

Quantiles or k-Values

From playlist g. The Normal Distribution and Two-Variable Statistics