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assignments:assignment3 [2016/09/19 12:19] asa [Part 1] |
assignments:assignment3 [2016/09/20 09:33] asa [Part 2] |
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==== Part 1 ==== | ==== Part 1 ==== | ||
- | Implement ridge regression in a class called RidgeRegression that implements the classifier API, i.e. ''fit'' and ''predict'' methods with the same signature as the Perceptron class you implemented in the previous assignment. Also implement functions for computing the following measures of error: | + | Implement ridge regression in a class called RidgeRegression that implements the classifier API, i.e. ''fit'' and ''predict'' methods with the same signature as the classifiers you implemented in the previous assignment. Also implement functions for computing the following measures of error: |
* The Root Mean Square Error (RMSE). | * The Root Mean Square Error (RMSE). | ||
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Regression Error Characteristic (REC) curves are an interesting way of visualizing regression error as described | Regression Error Characteristic (REC) curves are an interesting way of visualizing regression error as described | ||
in the following [[http://machinelearning.wustl.edu/mlpapers/paper_files/icml2003_BiB03.pdf|paper]]. | in the following [[http://machinelearning.wustl.edu/mlpapers/paper_files/icml2003_BiB03.pdf|paper]]. | ||
- | Write a function that plots the REC curve of a regression method, and plot the REC curve of the best regressor you found in Part 1 of the assignment. | + | Write a function that plots the REC curve of a regression method, and plot the REC curve of the best regressor you found in Part 1 of the assignment (i.e. the one that gave the lowest error on the validation set). Plot the REC curve for both the validation set and the training set. |
What can you learn from this curve that you cannot learn from an error measure such as RMSE or MAD? | What can you learn from this curve that you cannot learn from an error measure such as RMSE or MAD? | ||