Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Building Data Science Solutions with Anaconda
  • Toc
  • feedback
Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
close
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)
close
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

Explaining a model's outcome with LIME

Now we are moving on to black box models. They are becoming much more common due to the efficacy they have shown in popular areas of the domain, such as NLP, vision problems, and various other areas where vast amounts of data being fed in produce amazing results. These domains aren't going anywhere, and so we need to find a way to interpret these models after the fact using post-hoc interpretability.

The first approach that we'll look at is Local Interpretable Model-Agnostic Explanations (LIME), which assumes that if you zoom in on even a complex nonlinear relationship, you will find a linear one at the local level. It then will try to learn this local linear relationship by creating synthetic records that are like the record we care about. By creating these points/records that have slightly altered inputs, it can figure out the impact that each feature has based on the model's output. As the name suggests, its model agnostic...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete