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CSC59929- Assignment 2 Solved

•     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. 

•     The models we’ve looked at so far (Perceptron,  Adaline, and Logistic Regression are applicable to any number of features.

•     Using the Iris dataset, focus on the species Iris-virginica and Iris-versicolor.  These two classes are not linearly separable when you use only the two features petal length and sepal length.

•     Train the Adaline learning model using the following

•     All six cases of using two features at a time.

•     All four cases of using three features at a time.

•     The one case of using all features at once.

•     Do not use Scikit learn for this assignment.  You may, if you want, use the sample code that I’ve posted to Blackboard.

•     Summarize your results (i.e, what’ s the best accuracy you can obtain for each of the 11 cases you considered) in a table.

•     Discuss your findings.  Does using more dimensions help when trying to classify the data in this dataset?

•     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

•     Do not clear your results after you last run so that I well be able to see your results without rerunning your file.

If you collaborate with anyone on this assignment, be sure to follow the collaboration guidelines in the syllabus.

 

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