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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Testing the baseline model


Here, we will look at the code snippet that performs the actual testing. We will be obtaining all the testing matrices that have been explained so far. We are going to test all the different ML algorithms so that we can compare the accuracy score.

Testing of Multinomial naive Bayes

You can see the testing result for the multinomial naive Bayes algorithm in the following figure:

Figure 5.9: Code snippet for testing multinomial naive Bayes algorithm

As you can see, using this algorithm we have achieved an accuracy score of 81.5%.

Testing of SVM with rbf kernel

You can see the testing result for SVM with the rbf kernel algorithm in the following figure:

Figure 5.10: Code snippet for testing SVM with rbf kernel

As you can see, we have performed a test on the testing dataset and obtained an accuracy of 65.4%.

Testing SVM with the linear kernel

You can see the testing result for SVM with the linear kernel algorithm in the following figure:

Figure 5.11: Code snippet for testing...

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