CS4100 - Artificial Intelligence - Logistic Regression Algorithm - Alternative Algorithm - IT Assignment Help

Assignment Task:

CS4100:  IT Assignment Help

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3. Split the data set randomly into two equal parts, which will serve as the training set and the test set. Use your birthday (in the format MMDD) as the seed for the pseudorandom number generator. The same training and test sets should be used throughout this assignment.

4. Train your algorithm on the training set using independent random numbers in the range [−0.7, 0.7] as the initial weights. Find the MSE on the test set (it is defined by

(1) except that the average should be over the test rather than training set). Try different values of the learning rate η and of the number of training steps (so that your stopping rule is to stop after a given number of steps). Give a small table of test and training MSEs in your report.

5. Optional: Try different stopping rules, such as: stop when the value of the objective function (the training MSE) does not change by more than 1% of its initial value over the last 10 training steps.

6. Run your logistic regression program for a fixed value of η and for a fixed stopping rule (producing reasonable results in your experiments so far) 100 times, for different values of the initial weights (produced as above, as independent random numbers in [−0.7, 0.7]). In each of the 100 cases compute the test MSE and show it in your report as a boxplot.

7. Optional: Redo the experiments in items 4–5 modifying the training procedure as follows. Instead of training logistic regression once using Gradient Descent, train it 4 times using Gradient Descent with different values of the initial weights and then choose the prediction rule with the best training MSE.

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