February 2017
Intermediate to advanced
274 pages
5h 58m
English
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Parameter hyper-tuning
Rarely, our first model would be the best we can do. By simply looking at our metrics and accepting the model because it passed our pre-conceived performance thresholds is hardly a scientific method for finding the best model.
A concept of parameter hyper-tuning is to find the best parameters of the model: for example, the maximum number of iterations needed to properly estimate the logistic regression model or maximum depth of a decision tree.
In this section, we will explore two concepts that allow us to find the best parameters for our models: grid search and train-validation splitting.
Grid search
Grid search is an exhaustive algorithm that loops through the list of defined parameter values, estimates separate models, and ...
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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.