April 2018
Beginner to intermediate
282 pages
6h 52m
English
Content preview from Hands-On Automated Machine Learning
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Warm start
In terms of Automated ML (AutoML) pipelines, hyperparameter search space can grow really quickly and an exhaustive search becomes impracticable with limited time and finite resources. You need smarter ways to perform this task, especially if you have a large dataset with a complex model working on it. If you find yourself in this kind of situation, a GridSeachCV instances exhaustive search won't be feasible, or random parameter draws of RandomizedSearchCV might not give you the best results given limited time.
The basic idea of warm start is to use the information gained from previous training runs to identify smarter starting points for the next training run.
For example, LogisticRegression has a warm_start parameter, which is ...
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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.