Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Naïve Bayes classification


Let's get right into it! Let's begin with Naïve Bayes classification. This machine learning model relies heavily on results from previous chapters, specifically with Bayes theorem:

Let's look a little closer at the specific features of this formula:

  • P(H) is the probability of the hypothesis before we observe the data, called the prior probability, or just prior

  • P(H|D) is what we want to compute, the probability of the hypothesis after we observe the data, called the posterior

  • P(D|H) is the probability of the data under the given hypothesis, called the likelihood

  • P(D) is the probability of the data under any hypothesis, called the normalizing constant

Naïve Bayes classification is a classification model, and therefore a supervised model. Given this, what kind of data do we need?

  • Labeled data

  • Unlabeled data

(Insert jeopardy music here)

If you answered labeled data then you're well on your way to becoming a data scientist!

Suppose we have a data set with n features, (x1,...