II. NAÏVE BAYES. Dataset showing the class, attribute and number of evidences per class | attribute. Here's the deal: CLASS Banana Orange Other LONG 400 0 100 SWEET 350 150 150 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 YELLOW 450 300 50 TOTAL 500 300 200 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. Here's the deal: CLASS Banana Orange Other LONG 400 0 100 SWEET 350 150 150 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 YELLOW 450 300 50 TOTAL 500 300 200 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)
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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![II. NAÏVE BAYES.
Dataset showing the class, attribute and number of evidences per class | attribute.
Here's the deal:
CLASS
Banana
Orange
Other
LONG
400
0
100
SWEET
350
150
150
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
YELLOW
450
300
50
TOTAL
500
300
200
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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F120382c0-ca63-4e86-9430-209fedead7e8%2F0226320b-c5c2-4fe3-b0df-3b86749bed64%2Fsbqqxbi_processed.jpeg&w=3840&q=75)
Transcribed Image Text:II. NAÏVE BAYES.
Dataset showing the class, attribute and number of evidences per class | attribute.
Here's the deal:
CLASS
Banana
Orange
Other
LONG
400
0
100
SWEET
350
150
150
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
YELLOW
450
300
50
TOTAL
500
300
200
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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