Mastering Categorical Encoding: One-Hot, Label, Target, and K-Fold Explained 📊

In theory, discrete variables, or features, are easy to use with machine learning algorithms. However, in practice, it's not always so easy and we often have to transform discrete values, like favorite colors, into numbers. There are lots of ways to do this, and this video walks you through 3 of the most popular methods. English This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu. Spanish Este video ha sido doblado al español con voz artificial con https://aloud.area120.google.com para aumentar la accesibilidad. Puede cambiar el idioma de la pista de audio en el menú Configuración. Portuguese Este vídeo foi dublado para o português usando uma voz artificial via https://aloud.area120.google.com para melhorar sua acessibilidade. Você pode alterar o idioma do áudio no menu Configurações. 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 1:24 One-Hot Encoding 3:25 Label Encoding 4:39 Target Encoding 6:27 Target Encoding with a Weighted Mean, or Bayesian Target Encoding 9:56 K-Fold Target Encoding #StatQuest #DubbedWithAloud