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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Finding optimal hyperparameters with GridSearchCV

As we have created new models and tried various data processing techniques, we have used many different parameters and function arguments to determine how we set up the problem. One example is the impute method. Mean, median, or some other advanced approach – how do we know which we should take? One naïve approach might be to simply create a for loop and try every technique. We can calculate the score for each and use the best one. We tried a similar approach before when looking at which algorithm would give us the best score in the previous section.

This might be naïve, but never overlook the simple. It is such a good approach that scikit-learn decided to package that together and make an easy method to do so. It will even perform a k-fold cross-validation to make sure it is getting the best solution. There are a few different ways to tune hyperparameters, but we're going to focus on a grid search.

A grid...

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