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II. NAÏVE BAYES. Dataset showing the class, attribute and number of evidences per class|attribute. CLASS LONG SWEET YELLOW ТОTAL Banana 400 350 450 500 Orange 150 300 300 Other 100 150 50 200 Here's the deal: • We have a training dataset of 1,000 fruits. The fruit can be a Banana, Orange or Other The fruit can be Long, Sweet or Yellow Suppose we are told the unknown fruit is long, sweet and yellow, calculate for the probability of it being a banana, orange or other fruit.

II. NAÏVE BAYES. Dataset showing the class, attribute and number of evidences per class|attribute. CLASS LONG SWEET YELLOW ТОTAL Banana 400 350 450 500 Orange 150 300 300 Other 100 150 50 200 Here's the deal: • We have a training dataset of 1,000 fruits. The fruit can be a Banana, Orange or Other The fruit can be Long, Sweet or Yellow Suppose we are told the unknown fruit is long, sweet and yellow, calculate for the probability of it being a banana, orange or other fruit.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Database Question

Dataset showing the class, attribute and number of evidences per class|attribute.
CLASS LONG SWEET YELLOW TOTAL
Banana 400 350 450 500
Orange 0 150 300 300
Other 100 150 50 200
Here’s the deal:
• We have a training dataset of 1,000 fruits.
• The fruit can be a Banana, Orange or Other 
• The fruit can be Long, Sweet or Yellow

II. NAÏVE BAYES. Dataset showing the class, attribute and number of evidences per class attribute. CLASS LONG SWEET YELLOW ТОTAL Banana 400 350 450 500 Orange 150 300 300 Other 100 150 50 200 Here's the deal: We have a training dataset of 1,000 fruits. The fruit can be a Banana, Orange or Other The fruit can be Long, Sweet or Yellow Suppose we are told the unknown fruit is long, sweet and yellow, calculate for the probability of it being a banana, orange or other fruit.
Transcribed Image Text:II. NAÏVE BAYES. Dataset showing the class, attribute and number of evidences per class attribute. CLASS LONG SWEET YELLOW ТОTAL Banana 400 350 450 500 Orange 150 300 300 Other 100 150 50 200 Here's the deal: We have a training dataset of 1,000 fruits. The fruit can be a Banana, Orange or Other The fruit can be Long, Sweet or Yellow Suppose we are told the unknown fruit is long, sweet and yellow, calculate for the probability of it being a banana, orange or other fruit.
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