#### Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

## Chapter 2. Naive Bayes

A Naive Bayes classification algorithm assigns a class to an element of a set that is most probable according to Bayes' theorem.

Let's say that A and B are probabilistic events. P(A) is the probability of A being `true`. P(A|B) is the conditional probability of A being `true`, given that B is `true`. If this is the case, then Bayes' theorem states the following:

P(A) is the prior probability of A being `true` without the knowledge of the probability of P(B) and P(B|A). P(A|B) is the posterior probability of A being `true`, taking into consideration additional knowledge about the probability of B being `true`.

In this chapter, you will learn about the following topics:

• How to apply Bayes' theorem in a basic way to compute the probability of a medical test that is correct in the simple example medical test
• How to grasp Bayes' theorem by proving its statement and its extension
• How to apply Bayes' theorem differently to independent and dependent variables in examples of playing chess
• How...