Bias-Variance Trade-off - Explained

In this video we discuss why underfitting and overfitting happen in machine learning models. We look at the fundamental theory behind bias and variance, and try to derive the results from there. *Notes* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Between 03:30 - 03:45, on the bottom card, the parenthesis should say "Low Variance", not "High Variance" as it is in the video. Sorry for any confusion that this might have caused. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Why neural networks are universal functions approximators: https://youtu.be/O45AaRPQhuI Bagging vs Boosting: https://youtu.be/tjy0yL1rRRU Why neural networks are deep: https://youtu.be/Wss3WMy5OxE Why residual connections work: https://youtu.be/Gey9CG6R6w8 Why we need activations in neural nets: https://youtu.be/rj6K46u0J5w *Contents* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Intro 00:28 - Bias and Variance 01:45 - Bias and Variance Example 02:35 - The Bias Variance Trade-off 03:14 - Bias and Variance in Neural Nets/Linear Regression 04:32 - Outro *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic https://twitter.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #bias #variance #underfitting #overfitting #biasvariancetradeoff