HW04: Nonparametric Regression

HW04: Nonparametric Regression
In this homework, you will implement three nonparametric regression algorithms in R, Matlab, or Python. Here are the steps you need to follow: 1. You are given a univariate data set, which contains 133 data points, in the file named hw04_data_set.csv. Randomly pick 100 data points as the training set and the remaining 33 data points will be used as the test set. 2. Learn a regressogram by setting the bin width parameter to 3. Draw training data points, test data points, and your regressogram in the same figure. Your figure should be similar to the following figure.
3. Calculate the root mean squared error for test data points. The formula for RMSE can be written as: RMSE= (𝑦(−𝑦()+ ,-./(01𝑁3453 Your output should be similar to the following sentence. ## [1] "Regressogram = RMSE is 24.3717 when h is 3"
4. Learn a running mean smoother by setting the bin width parameter to 3. Draw training data points, test data points, and your running mean smoother in the same figure. Your figure should be similar to the following figure.

5. Calculate the root mean squared error for test data points. Your output should be similar to the following sentence. ## [1] "Running Mean Smoother = RMSE is 21.9220 when h is 3"
6. Learn a kernel smoother by setting the bin width parameter to 1. Draw training data points, test data points, and your kernel smoother in the same figure. Your figure should be similar to the following figure.
7. Calculate the root mean squared error for test data points. Your output should be similar to the following sentence. ## [1] "Kernel Smoother = RMSE is 22.1088 when h is 1"
What to submit: You need to submit your source code in a single file (.R file if you are using R, .m file if you are using Matlab, or .py file if you are using Python) and a short report explaining your approach (.doc, .docx, or .pdf file). You will put these two files in a single zip file named as STUDENTID.zip, where STUDENTID should be replaced with your 7-digit student number. How to submit: E-mail the zip file you created to mehmetgonen@ku.edu.tr with the subject line Intro2MachineLearningHW04. Please follow the exact style mentioned for the subject line and do not send a zip file named as STUDENTID.zip.