STATISTICS 452/652 Project 2 Solved

The file will be a comma-separated file (csv) that contains n observations of p explanatory variables (labeled X1–Xp) and one response (Y). That’s all I’m telling you about the data.

Your job is to develop a method for predicting Y based on X. You may use any of the techniques covered in class.

1.    I will post a test set of explanatory variables without the response variable attached.You will return a list of predicted values, in the same order. The list should be one column of numbers with no row numbers and no column header. Use write.table(predictions, file.name, sep = ",", row.names = F, col.names = F)
edictions. This gets posted to Crowdmark. Details are given below.

1.    What models or machines did you attempt to fit? For each one, paste the R code from your program for the initial successful model fit. I want to see what you tried. For example, "fit1 = lm(y~., data=train)" is what I would list if I used multiple linear regression on all of the variables and my training data were called "train". The answer should be a list (e.g., with bullets) of nothing but the code for each of these model fits. Don’t list code that did not run. If tuning was involved in the initial fitting process, you can can paste the function with variable names for the tuning parameters (e.g., your function might have "mtry=mm" if you looped over a variable called "mm").

2.    What process(es) did you use to evaluate and compare models and to select your final model? I am thinking of Lecture 3, specifically: Give 1-2 sentences explaining the method, the quantity, how results were turned into decisions. For example, "I used 50,000 bootstrap resamples, fit all models to each resample, and used largest training error from last resample as my best model." (This example answer is complete, but represents something rather stupid to do...)

3.    Did you tune any methods? If so, (a) what process(es) did you use to evaluate and compare models and to select your final model (i.e., I want to see an answer like to the previous question, but relating to how TUNING was done), and (b) for each method list all parameter values that were considered (e.g., "For "Blasting" I use a grid of values with A=(1, 2, 3, ... , 60) and B=(0.00317, sqrt(3.14159)). For "Blooming" I used combinations of (z,γ)=(0.1, 3), (0.5, 6), and (1.1, 12) ).

4.    What was your chosen prediction machine? Paste the code that produced your predicted values, including all values of tuning parameters if any, random number seeds, and explaining any variable names that are not obvious. I should be able to run your code and produce the same results (or extremely similar if randomization is used). If I try and it doesn’t work, there will be a major deduction.

5.  List the variables that you believe are important. A positive bonus will be given for each correct results. A deduction will be made for each variable listed that was not important.

The main thing here is that I should be able to see what your thought process was and whether you considered (or failed to consider) important ideas.