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# JGS594 Lecture 21

Software Engineering for Machine Learning
Naive Bayes
(202204)

April 20, 2022

## Transcript

1. ### jgs SER 594 Software Engineering for Machine Learning Lecture 21:

Classification Dr. Javier Gonzalez-Sanchez [email protected] javiergs.engineering.asu.edu | javiergs.com PERALTA 230U Office Hours: By appointment
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jgs Machine Learning
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jgs Definition § Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. § Classification – approximating a mapping function (f) from input variables (X) to discrete output variables (y). § Regression – approximating a mapping function (f) from input variables (X) to a continuous output variable (y). § Discretization – convert a regression problem to a classification problem
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jgs But, How does a Classifier work?
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jgs Previously / / yes, we are still using Mallet. / / Nothing else.

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jgs Naïve Bayes § It is one of the most popular and simple machine learning classification algorithms § based on the Bayes Theorem for calculating probabilities and conditional probabilities § It can be extremely fast relative to other classification algorithms § It is easy to build and particularly useful for very large data sets § The name naive is used because it assumes the features that go into the model is independent of each other. That is changing the value of one feature, does not directly influence or change the value of any of the other features used in the algorithm.
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jgs Bayes X Y Frequency Table Likelihood Table Example: P (Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny) P (Yes | Sunny) = 3 / 9 * 9 / 14 / 5/14 = 0.60 Bayes Rule:
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jgs Outlook Temperat ure Humidity Windy Play Golf 0 Rainy Hot High False No 1 Rainy Hot High True No 2 Overcast Hot High False Yes 3 Sunny Mild High False Yes 4 Sunny Cool Normal False Yes 5 Sunny Cool Normal True No 6 Overcast Cool Normal True Yes 7 Rainy Mild High False No 8 Rainy Cool Normal False Yes 9 Sunny Mild Normal False Yes 10 Rainy Mild Normal True Yes 11 Overcast Mild High True Yes 12 Overcast Hot Normal False Yes 13 Sunny Mild High True No X1 X2 X3 X4 Y Frequency Table
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jgs Naïve Bayes Bayes Rule: Naïve Bayes Rule:
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jgs Naïve Bayes Example: P (Yes | Rainy, Hot, High, False) = P( Rainy | Yes) * P( Hot | Yes) * P( High | Yes) * P( False | Yes) * P(Yes) / P (Rainy) * P (Hot) * P (High) * P (False) = 3/9 * 2/9* 3/9 * 6/9 * 9/14 / 5/14* 4/14 * 7/14 * 8/14 = 0.01646* 0.6428 / 0.0291 = 0.3629 P (No | Rainy, Hot, High, False) = P( Rainy | No) * P( Hot | No) * P( High | No) * P( False | No) * P(No) / P (Rainy) * P (Hot) * P (High) * P (False) = 2/5 * 2/5* 4/5 * 2/5 * 5/14 / 5/14* 4/14 * 7/14 * 8/14 = 0.0512* 0.3571 / 0.0291 = 0.6282
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jgs Weka | NaiveBayes CSV file

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jgs Weka | NaiveBayes | Evaluation

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jgs Decision Tree § Also known as Classification And Regression Trees (CART) § Learning answers to a hierarchy of if/else questions leading to a decision

jgs Example
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jgs Example Which Attribute Should be the ROOT?

jgs Data
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jgs Entropy Entropy is a measurement of the degree of randomness or the increase in the disorganization within a system.

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jgs Questions
23. ### jgs SER 594 Software Engineering for Machine Learning Javier Gonzalez-Sanchez,

Ph.D. [email protected] Spring 2022 Copyright. These slides can only be used as study material for the class CSE205 at Arizona State University. They cannot be distributed or used for another purpose.