Estimation methods | Linear algebra | Regression analysis | Inverse problems
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. It has been used in many fields including econometrics, chemistry, and engineering. Also known as Tikhonov regularization, named for Andrey Tikhonov, it is a method of regularization of ill-posed problems. it is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers of parameters. In general, the method provides improved efficiency in parameter estimation problems in exchange for a tolerable amount of bias (see bias–variance tradeoff). The theory was first introduced by Hoerl and Kennard in 1970 in their Technometrics papers “RIDGE regressions: biased estimation of nonorthogonal problems” and “RIDGE regressions: applications in nonorthogonal problems”. This was the result of ten years of research into the field of ridge analysis. Ridge regression was developed as a possible solution to the imprecision of least square estimators when linear regression models have some multicollinear (highly correlated) independent variables—by creating a ridge regression estimator (RR). This provides a more precise ridge parameters estimate, as its variance and mean square estimator are often smaller than the least square estimators previously derived. (Wikipedia).
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From playlist Statistical Regression
10 Machine Learning: Ridge Regression
Lecture on ridge regression with a focus on variance and bias trade-off and hyper parameter tuning. Follow along with the demonstration workflow in Python's scikit-learn package: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_RidgeRegression.ipynb
From playlist Machine Learning
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
Regularization Part 1: Ridge (L2) Regression
Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model to the training data. It can also help you solve unsolvable equations, and if that isn't bad to the bone, I don't know what is. This StatQuest follows up on
From playlist StatQuest
How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
From playlist Linear Regression.
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.
Ridge vs Lasso Regression, Visualized!!!
People often ask why Lasso Regression can make parameter values equal 0, but Ridge Regression can not. This StatQuest shows you why. NOTE: This StatQuest assumes that you are already familiar with Ridge and Lasso Regression. If not, check out the 'Quests. Ridge: https://youtu.be/Q81RR3yKn
From playlist StatQuest
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
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 some modern regression techniques including a discussion of the bias varianc
From playlist Data Science
Regularization Part 2: Lasso (L1) Regression
Lasso Regression is super similar to Ridge Regression, but there is one big, huge difference between the two. In this video, I start by talking about all of the similarities, and then show you the cool thing that Lasso Regression can do that Ridge Regression can't. NOTE: This StatQuest fo
From playlist StatQuest
Regularization Part 3: Elastic Net Regression
Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained
From playlist StatQuest
Ridge, Lasso and Elastic-Net Regression in R
The code in this video can be found on the StatQuest GitHub: https://github.com/StatQuest/ridge_lasso_elastic_net_demo/blob/master/ridge_lass_elastic_net_demo.R This video is assumes you already know about Ridge, Lasso and Elastic-Net Regression, if not, here are the links to the Quests..
From playlist Statistics and Machine Learning in R
Statistical Learning: 6.7 The Lasso
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 6.6 Shrinkage methods and ridge regression
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
My Patreon : https://www.patreon.com/user?u=49277905
From playlist Statistical Regression
Regularization In Machine Learning | Regularization Example | Machine Learning Tutorial |Simplilearn
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