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Big Data Analytics with Java

You're reading from   Big Data Analytics with Java Data analysis, visualization & machine learning techniques

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787288980
Length 418 pages
Edition 1st Edition
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Concepts
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Author (1):
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RAJAT MEHTA RAJAT MEHTA
Author Profile Icon RAJAT MEHTA
RAJAT MEHTA
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Table of Contents (15) Chapters Close

Preface 1. Big Data Analytics with Java FREE CHAPTER 2. First Steps in Data Analysis 3. Data Visualization 4. Basics of Machine Learning 5. Regression on Big Data 6. Naive Bayes and Sentiment Analysis 7. Decision Trees 8. Ensembling on Big Data 9. Recommendation Systems 10. Clustering and Customer Segmentation on Big Data 11. Massive Graphs on Big Data 12. Real-Time Analytics on Big Data 13. Deep Learning Using Big Data Index

Conditional probability


Conditional probability in simple terms is the probability of occurrence of an event given that another event has already occurred. It is given by the following formula:

P(B|A)= P(A and B)/P(A)

Here in this formula the values stand for:

Probability value

Description

P(B|A)

This is the probability of occurrence of event B given that event A has already occurred.

P(A and B)

The probability that both event A and B occur.

P(A)

This is the probability of occurrence of an event A.

Now let's try to understand this using an example. Suppose we have a set of seven figures as follows:

As seen in the preceding figure, we have three triangles and four rectangles. So if we randomly pull one figure from this set the probability that it belongs to either of the figures will be:

P(triangle) = Number of Triangles / Total number of figures = 3 / 7

P(rectangle) = Number of rectangles / Total number of figures = 4 / 7

Now suppose we break the figure into two individual sets...

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