November 2018
Beginner to intermediate
182 pages
4h 48m
English
Content preview from Natural Language Processing with Python Quick Start Guide
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Ensembling models
Ensembling models is a very powerful technique for improving your model performance across a variety of machine learning tasks.
In the following section, we have quoted from the Kaggle Ensembling Guide (https://mlwave.com/kaggle-ensembling-guide/) written by MLWave.
We can explain why ensembling helps to reduce error or improve accuracy, as well as demonstrate the popular techniques on our chosen task and dataset. While each of these techniques might not result in a performance gain for us on our dataset specifically, they are still a powerful tool to have in your mental toolkit.
To ensure that you understand these techniques, we strongly urge you to try them on a few datasets.
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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.
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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.