• 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.
