Description
Programming Exercise
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Up to this point we’ve been looking at only two features at a time. We’ve done this largely so that we can visualize the decision boundary. With only two features, the decision boundary is a line in the plane defined by the two features.
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The models we’ve looked at so far (Perceptron, Adaline, and Logistic Regression are applicable to any number of features.
The City College of New York
CSc 59929 – Introduction to Machine Learning 2 Spring 2020 – Erik K. Grimmelmann, Ph.D.
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Using the Iris dataset, focus on the species Iris-setosa and Iris-versicolor. These two classes are not linearly separable when you use only the two features petal length and sepal length.
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Train the Adaline learning model using the following
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All six cases of using two features at a time.
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All four cases of using three features at a time.
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The one case of using all features at once.
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Do not use Scikit learn for this assignment. You may, if you want, use the sample code that I’ve posted to Blackboard.
The City College of New York
CSc 59929 – Introduction to Machine Learning 3 Spring 2020 – Erik K. Grimmelmann, Ph.D.
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Summarize your results (i.e, what’ s the best accuracy you can obtain for each of the 11 cases you considered) in a table.
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Discuss your findings. Does using more dimensions help when trying to classify the data in this dataset?
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Include all of your analysis and discussion in your .ipynb file and submit the file through Blackboard. The name of your file should be
firstname_lastname_AS02.ipynb
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Do not clear your results after you last run so that I well be able to see your results without rerunning your file.
The City College of New York
CSc 59929 – Introduction to Machine Learning 4 Spring 2020 – Erik K. Grimmelmann, Ph.D.
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 5 Spring 2020 – Erik K. Grimmelmann, Ph.D.
