January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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Bayesian hyperparameter optimization
In the Tuning hyperparameters using grid search and cross-validation recipe in Chapter 8, Identifying Credit Default with Machine Learning, we described how to use grid search and randomized search to find the (possibly) best set of hyperparameters for our model. In this recipe, we introduce another approach to finding the optimal set of hyperparameters, this time based on the Bayesian methodology. The main motivation for the Bayesian approach is that both grid search and randomized search make uninformed choices, either through exhaustive search over all combinations or through a random sample. This way, they spend a lot of time evaluating bad (far from optimal) combinations, thus basically wasting time. ...
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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.