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You're reading from  Statistics for Machine Learning

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Published inJul 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781788295758
Edition1st Edition
Languages
Python
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Statistics
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Pratap Dangeti
Pratap Dangeti
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Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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Probability fundamentals

Before diving into Naive Bayes, it would be good to reiterate the fundamentals. Probability of an event can be estimated from observed data by dividing the number of trails in which an event occurred with total number of trails. For instance, if a bag contains red and blue balls and randomly picked 10 balls one by one with replacement and out of 10, 3 red balls appeared in trails we can say that probability of red is 0.3, pred = 3/10 = 0.3. Total probability of all possible outcomes must be 100 percent.

If a trail has two outcomes such as email classification either it is spam or ham and both cannot occur simultaneously, these events are considered as mutually exclusive with each other. In addition, if those outcomes cover all possible events, it would be called as exhaustive events. For example, in email classification if P (spam) = 0.1, we will be able to calculate P (ham) = 1- 0.1 = 0.9, these two events are mutually exclusive. In the following Venn diagram, all...

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Statistics for Machine Learning
Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758
© 2017 Packt Publishing Limited All Rights Reserved

Author (1)

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Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Read more about Pratap Dangeti

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