_iai12-17-entropy-remainder.jpg "IAI12-17 - Entropy Remainder from calculate information entropy Video Screenshot Preview")
GalleryPlay Video:
(Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)
⏲ Duration: 23 min 30 sec
✓ Published: 22-Nov-2012
✓ Published: 22-Nov-2012
Description:
This lecture covers how you calculate the remaining entropy, and then information gain, when you split a single data set into multiple ones by selecting a decision tree attribute.nn-----------------nnThese videos are recordings of lectures from the module Introduction to AI run by The School of Computer Science at the University of Birmingham, UK.nnMore information at: http://www.cs.bham.ac.uk/internal/courses/intro-ai
This lecture covers how you calculate the remaining entropy, and then information gain, when you split a single data set into multiple ones by selecting a decision tree attribute.nn-----------------nnThese videos are recordings of lectures from the module Introduction to AI run by The School of Computer Science at the University of Birmingham, UK.nnMore information at: http://www.cs.bham.ac.uk/internal/courses/intro-ai
Share with your friends:
Whatsapp | Viber | Telegram | Line | SMS
Email | Twitter | Reddit | Tumblr | Pinterest
Related Videos
_intuitively-understanding-the-shannon-entropy.webp "Adian Liusie")
_entropy-for-data-science-clearly-explained.webp "StatQuest with Josh Starmer")
_entropy-calculation-part-1-intro-to-machine-learning.webp "Udacity")
_how-to-find-the-entropy-and-information-gain-in-decision-tree-learning-by-mahesh-huddar.webp "Mahesh Huddar")
⏲ 12 minutes 15 seconds 👁 109.4K
_intuitively-understanding-the-shannon-entropy.jpg)
_entropy-for-data-science-clearly-explained.jpg)
_entropy-calculation-part-1-intro-to-machine-learning.jpg)
_how-to-find-the-entropy-and-information-gain-in-decision-tree-learning-by-mahesh-huddar.jpg)
![[+]](/images/menu.gif){kind=small}