Homework answers / question archive / CSE 6363: Machine Learning University of Texas at Arlington Summer 2022 Alex Dillhoff Assignment 1 This assignment covers linear regression and logistic regression using disciminative methods

CSE 6363: Machine Learning University of Texas at Arlington Summer 2022 Alex Dillhoff Assignment 1 This assignment covers linear regression and logistic regression using disciminative methods

Statistics

Share With

CSE 6363: Machine Learning
University of Texas at Arlington
Summer 2022
Alex Dillhoff
Assignment 1
This assignment covers linear regression and logistic regression using disciminative methods.
1 The Iris Dataset
The Iris flower data set (https://en.wikipedia.org/wiki/Iris_flower_data_set) was organized
by Ronald Fisher in 1936. It is a commonly used dataset for introductory machine
learning concepts. You will use this dataset for use with a classification AND regression
task.
1.1 Preparing the Data
To begin, load the data using scikit-learn. As we saw during class, the setosa samples
are very clearly linearly separable given any combination of two features. However, the
versicolor and virginica usually have some overlap.
In order to verify the models that you will create in the following two sections, you will
need to take some portion of the dataset and reserve it for testing. Randomly select 10%
of the dataset, ensuring an even split of each class. This will be your test set. The rest
of the data will serve as your training set.
2 Regression
Fit 12 linear regression models to the training data with parameters w = [w0;w1] for each
one. For example, your first model may use sepal length as the input feature to predict
sepal width and the second model would reverse that combination. Some of these input
features will not be good predictors.
2.1 Model Definition
Your implementation should define a class for LinearRegression which includes at least
a fit and predict method. Additional methods can be added as you see fit.
The fit method should accept 2 required parameters: the input data and target values.
Other parameters can be added as long as they are optional.
CSE 6363: Assignment 1 Dillhoff
2.2 Training
Your models should be trained using batch gradient descent with a batch size (optional
parameter) of 32. Use mean squared error as your loss function. For each model, train for
n = 100 steps (optional parameter). As each model trains, record the loss average over
the batch size against the current step number. One way to save this data is to either
return an array from the fit method or save it as an internal class member that can be
retrieved after training is complete. Plot the loss against the step number and save
it. This will go in your report.
To observe the effects of regularization, pick one of your trained models and inspect the
weights. Train an identical model again, except this time you will add L2 regularization to
the loss. Record the difference in parameters between the regularized and non-regularized
model. In your report, include the weight values in this comparison.
2.3 Testing
For each model you created, test its performance on unseen data by evaluating the mean
squared error against the test dataset that you set aside previously. Based on these results,
which input feature is most predictive of its corresponding output feature? Create a
table of results that summarized the testing accuracy of each model and put
it in your report.
3 Classification
For classification, you will implement Linear Discriminant Analysis, Logistic Regression,
and Naive Bayes models for classification. Your implementation should define a class for
each model which includes at least a fit and predict method. Additional methods can
be added as you see fit.
3.1 Training
The models can be fit following the parameter fitting methods discussed in class for each
respective model.
3.2 Testing
For each model you created, test its performance on unseen data by evaluating the mean
squared error against the test dataset that you set aside previously. Based on these results,
which input feature is most predictive of its corresponding output feature? Create a
table of results that summarized the testing accuracy of each model and put
it in your report.
Submission
Create a zip file that includes all of your code as well as your report. The TA should
be able to easily run the code to reproduce all plots and results. Include any additional
instructions, if necessary.