Machine Learning Tutorial Python 20 Bias vs Variance In Machine Learning

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http://youtube.com/watch?v=EuBBz3bI-aA

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: / statquest • ...or... • YouTube Membership: / @statquest • ...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: • / 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

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