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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

Overcoming sample bias

Sample bias is when the choice of data doesn't reflect what is present in the real world. This is also referred to as selection bias. As with many types of bias, this can be completely harmless or very impactful, depending on the application.

In the following diagram, you can see a visual representation of what this looks like. There is hypothetical real-world data on the left that would be helpful (represented as Input z), but for one reason or another, it did not make it into the data that is included in the training dataset:

Figure 6.2 – Sample bias

When we leave this valuable data out, it is detrimental to everyone involved. The previous diagram is more abstract, so let's look at some more concrete examples of what sample bias could look like.

Examples of sample bias

The following items are examples of where sample bias could exist. Of course, this isn't close to an exhaustive list but helps to give...

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