Due Mar 4th, 6pm
Code To receive full credit all unit tests must pass and one copy of the exercises must be completed.
Exercises: 6.4.2, 6.4.3, 6.6.1, 6.7, 6.9.1, 6.12.1, 6.12.2, 6.14.1, 6.19.1
Clone the repo into a local directory named
homework_03. Do not use the original repo name. Replace
X below with your team name.
git clone https://github.com/columbia-applied-data-science/homework_03_team_X.git \ homework_03
demo.py and the tests to get an idea of how things work.
See this section in the lecture notes information about pseudo inverses.
5-fold cross validation
You will make a 5-fold cross validation module. This is used as a way to pick out your regularization parameter delta. Our 5-fold cross validation is:
For every delta:
- Divide the data up into 5 equal chunks
- Pick out the first chunk as a cross-validation set, and group the other 4 together as training data.
- Fit the model using the training data and use the cross validation set to measure both the training and cross-validation squared error |Xw - Y|^2
- Repeat 5 times, each time using a different chunk as the cross validation set.
- Average the training and cross-validation errors across the 5 folds.
Compare the average cross-validation errors and use this to choose delta. Note that the training error should not be used to choose delta. It is there to serve as a reality check and to diagnose the degree of over/under fitting.
These routines are very picky about array shape. Some functions, e.g. np.dot, return arrays who have shape = (N,) (a tuple with only one element). In that case, you will often have to reshape this into a proper two dimensional array. The docstring for linear_reg.fit() tells you when to do this.
Two functions, linearreg.fit() and crossvalidator.cross_val() can handle pandas objects as their input. The others may or may not. However, these are the only public methods in their modules, so this is ok.
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