July 2017
Beginner to intermediate
486 pages
13h 49m
English
Content preview from Python Natural Language Processing
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Underfitting
In this section, we will discuss the term underfitting. What is underfitting and how is it related to the bias-variance trade-off?
Suppose you train the data using any ML algorithm and you get a high training error. Refer to Figure 8.60:
Figure 8.60: Graph indicating a high training error (Image credit: http://www.learnopencv.com/wp-content/uploads/2017/02/bias-variance-tradeoff.png)
The preceding situation, where we get a very high training error, is called underfitting. ML algorithms just can't perform well on the training data. Now, instead of a linear decision boundary, we will try a higher degree of polynomials. Refer to ...
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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.