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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...

Key benefits

Use Python and its wide array of machine learning libraries to build predictive models

Learn the basics of the 7 most widely used machine learning algorithms within a week

Know when and where to apply data science algorithms using this guide

Description

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

What you will learn

Understand how to identify a data science problem correctly

Implement well-known machine learning algorithms efficiently using Python

Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy

Devise an appropriate prediction solution using regression

Work with time series data to identify relevant data events and trends

Cluster your data using the k-means algorithm

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

€32.99

€36.99

Artificial Intelligence and Machine Learning Fundamentals

€24.99

Total € 94.97

Placeholder Nov 27, 2018

The book is good and explains in detail

Amazon Verified review

Rajesh Feb 23, 2019

Algorithm presented with great clarity

yassine chaachaa Nov 03, 2020

I am quite disappointed with the book. Yes it has main concepts and algorithms of data science. However, I cannot see the grids or data in the map; for example from page 17 to 20. The map of Italy does not have any data on it. and if it does, I cannot see it. I understand it is being made in black and white to save money. I bought R for data science book and it is all colours (500 pages) for the same price while this book is only 200 pages but they cannot make pages in colours. great disappointment.

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Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning , Second Edition

What do you get with Print?

Data Science Algorithms in a Week

Naive Bayes

Medical tests – basic application of Bayes' theorem

Analysis

Bayes' theorem and its extension

Bayes' theorem

Proof

Playing chess – independent events

Implementation of a Naive Bayes classifier

Playing chess – dependent events

Gender classification – Bayes for continuous random variables

Analysis

Summary

Medical tests – basic application of Bayes' theorem

Analysis

Page 1 of 9

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Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning , Second Edition

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Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Contact Details

Shipping Address

Billing Address

Product Details

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Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

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About the authors

FAQs