Description
Lab 3
Use Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) over the
provided dataset. You will use Python, Numpy, Sci-kit Learn, and Matplotlib 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:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
— Iris Setosa
— Iris Versicolour
— Iris Virginica
Exercises:
1. Break the sample into 80% for training, and 20% for test datasets. Choose the first 80%
instances from each class for training and the remaining 20% for testing.
a. Hint: make sure your initial representation of the data set (of type List[List[]])
passes the provided test_dataset function. This is not required, since you can
substitute the exact types (np.float64 instead of float, int instead of str), but it is a
step in the right direction.
2. Build a LDA classifier based on the training data. Use the appropriate classifier built into
sci-kit learn. Report the training and test errors.
a. Make a function that returns your trained classifier. Train solely over the training
data.
b. Note that, when passing a numpy array of samples into a given classifier’s
predict() function, you may run into an error regarding casting values to
np.float64. If you run into this, use samples = samples.astype(np.float64).
3. Build a QDA classifier based on the training data. Use the appropriate classifier built into
sci-kit learn. Report the training and test errors.
a. Train solely over the training data.
4. Are any of the variables not important in classifying iris type? Explain your answer based
on your experiments.
