Machine learning algorithms | Computational statistics | Ensemble learning
Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling with replacement to create training samples for the model to learn from. OOB error is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample. Bootstrap aggregating allows one to define an out-of-bag estimate of the prediction performance improvement by evaluating predictions on those observations that were not used in the building of the next base learner. (Wikipedia).
I guess this is what happens when you don't put anything up to keep the shopping carts from falling out of the truck.
From playlist Inertia
Yes. I make mistakes ... rarely. http://www.flippingphysics.com
From playlist Miscellaneous
Definition of an Outlier in Statistics MyMathlab Homework Problem
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Definition of an Outlier in Statistics MyMathlab Homework Problem
From playlist Statistics
Overfitting 2: training vs. future error
[http://bit.ly/overfit] Training error is something we can always compute for a (supervised) learning algorithm. But what we want is the error on the future (unseen) data. We define the generalization error as the expected error of all possible data that could come in the future. We cannot
From playlist Overfitting
GCSE Science Revision "Systematic Errors"
In this video, we look at systematic errors. First we explore what is meant by a systematic error. We then look at what can cause a systematic error, including a zero error. Image Credits Thermometer https://commons.wikimedia.org/wiki/File:Laboratory_thermometer-03.jpg Lilly_M, CC BY-SA
From playlist GCSE Working Scientifically
Watch this idiot get caught falling asleep at work by a security camera.
From playlist Funny Videos, Parodies, Odds and ends!
Overfitting 3: confidence interval for error
[http://bit.ly/overfit] The error on the test set is an approximation of the true future error. How close is it? We show how to compute a confidence interval [a,b] such that the error of our classifier in the future is between a and b (with high probability, and under the assumption that f
From playlist Overfitting
Statistics - How to find outliers
This video covers how to find outliers in your data. Remember that an outlier is an extremely high, or extremely low value. We determine extreme by being 1.5 times the interquartile range above Q3 or below Q1. For more videos visit http://www.mysecretmathtutor.com
From playlist Statistics
Statistical Learning: 8.4 Bagging
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
15 Machine Learning: Random Forest
Lecture on machine learning with ensemble tree methods. Tree bagging and random forest to mitigate against the model variance component of model inaccuracy in testing! Follow along with the demonstration in Python: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Subsurface
From playlist Machine Learning
Maxwell Hutchinson: "Boltzmann Trees: a physically inspired randomization for robust modeling of..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Boltzmann Trees: a physically inspired randomization for robust modeling of physical data" Maxwell Hutchinson - Citrine Informatics, Scientific Software Engineering
From playlist Machine Learning for Physics and the Physics of Learning 2019
Machine Learning Lecture 31 "Random Forests / Bagging" -Cornell CS4780 SP17
Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote18.html If you want to take the course for credit and obtain an official certificate, there is now a revamped version (with much higher quality videos) offered through eCornell ( https://tinyurl.com/eCornel
From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
Statistical Learning: 8.R.2 Random Forests and Boosting
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
StatQuest: Random Forests in R
Random Forests are an easy to understand and easy to use machine learning technique that is surprisingly powerful. Here I show you, step by step, how to use them in R. NOTE: There is an error at 13:26. I meant to call "as.dist()" instead of "dist()". The code that I used in this video ca
From playlist Statistics and Machine Learning in R
Statistics - 10.1.4 Errors in Hypothesis Testing
This is a very high-level view of errors in hypothesis testing. There is much more to know with errors, but we leave that for a higher-level statistics course. Power Point: https://bellevueuniversity-my.sharepoint.com/:p:/g/personal/kbrehm_bellevue_edu/EbVdAag0EIVNsC0fYjWHvrsBuIT_5cEC2I_t
From playlist Applied Statistics (Entire Course)
How convolutional neural networks work, in depth
Part of the End-to-End Machine Learning School Course 193, How Neural Networks Work at https://e2eml.school/193 slides: https://docs.google.com/presentation/d/1R-DnrghbU36jO8X4scbrrlx6gFyJHgSL3bD274sutng/edit?usp=sharing machine learning blog: https://brohrer.github.io/blog.html
From playlist E2EML 193. How Neural Networks Work