Machine Learning Fundamentals: Bias and Variance

Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 0:29 The data and the "true" model 1:23 Splitting the data into training and testing sets 1:40 Least Regression fit to the training data 2:16 Definition of Bias 2:33 Squiggly Line fit to the training data 3:40 Model performance with the testing dataset 4:06 Definition of Variance 5:10 Definition of Overfit Correction: 4:06 I say that the difference in fits between the training dataset and the testing dataset is called Variance. However, I should have said that the difference is a _consequence_ of variance. Technically, variance refers to the amount by which the predictions would change if we fit the model to a different training data set. #statquest #biasvariance #ML