Description
Implement Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). You can use Python, R or Matlab for this assignment. Please do not use any machine learning library for this assignment. Perform classifications on the Iris dataset which can be downloaded at http://www.cse.scu.edu/~yfang/coen140/iris.data
The dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
Attribute Information:
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sepal length in cm
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sepal width in cm
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petal length in cm
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petal width in cm
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class:
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Iris Setosa
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Iris Versicolour
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Iris Virginica
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Exercises:
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Break the sample into 80% for training, and 20% for test datasets. You can choose the first 80% instances from each class for training and the rest for testing.
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Build an LDA classifier based on the training data. Report the training and test errors for your classifier.
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Build a QDA classifier based on the training data. Report the training and test errors for your classifier.
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Is there any class linearly separable from other classes? Explain your answer based on your experiments.
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Are any of the variables not important in classifying iris type? Explain your answer based on your experiments.
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Assume the features are independent, i.e., ∑ is a diagonal matrix. Repeat 2 and 3, and report your results.