Description
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Compete the two programming exercises described in the following charts.
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Start looking for an interesting dataset for your project.
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Apply the Scikit Learn SVM Classifier to the Iris dataset using all three categories and all four feature at once and upload your .ipynb file.
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Run the SVM model (at least) four times using a different kernel each time. Compare and discuss the results for each of the kernels.
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Name your file lastname_firstname_AS04A.ipynb.
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Apply the Scikit Learn Decision Tree Classifier to the Iris dataset using all three categories and all four feature at once and upload your .ipynb file.
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See if your choice of impurity measure makes a difference in your results.
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Name your file lastname_firstname_AS04B.ipynb.
Programming Exercises (both A and B)
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Discuss your findings.
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Include all of your discussion in your .ipynb file and submit the file through Blackboard.
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Do not clear your results after you last run so that I will be able to see your results without rerunning your code.
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If you collaborate with anyone on this assignment, be sure to follow the collaboration guidelines in the syllabus.
The City College of New York
CSc 59929 – Introduction to Machine Learning 4 Spring 2020 – Erik K. Grimmelmann, Ph.D.
