January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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K-fold cross-validation
Let's walk through the steps of k-fold cross-validation:
- The data is divided into k-subsets.
- One set is kept for testing/development and the model is built on the rest of the data (k-1). That is, the rest of the data forms the training data.
- Step 2 is repeated k-times. That is, once the preceding step has been performed, we move on to the second set and it forms a test set. The rest of the (k-1) data is then available for building the model:
4. An error is calculated and an average is taken over all k-trials.
Every subset gets one chance to be a validation/test set since most of the data is used as a training set. ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.