Goal:
To find the best classifier from Logistic Regression and its variants and K-Nearest Neighbors and its variants that can most accurately classify the digits in the Digits dataset (https://scikit- learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) from the scikit-learn library.
Problem Description:
To find the best digit classifier, you need to first load the dataset, and perform the appropriate train-validation-test split. Since the dataset is relatively small and has a moderate number of features (64), you will need to apply k-fold-cross validation to stabilize the results. After splitting the dataset into these parts, you should normalize the parts using the training set, and train the candidate classifier on the training set. Then find the accuracy of the trained classifier on both the train and the validation set. The difference will tell you if your classifier is overfitting or not. Additionally, save the validation accuracy. Repeat the experiment for k-folds and save each of the validation accuracy you obtain from these folds. Then compute the mean (average) validation accuracy. This mean validation accuracy is an indicator of the goodness of your candidate model. Try with different models (e.g. simple logistic regression (LR), LR with squared features, LR with squared and cubed features (selective), K-nearest neighbors with different values of K, etc. — you are welcome to try out other classifiers) to find the one that yields the best mean validation accuracy. Then compute the mean test set accuracy using that best classifier and report that in the appropriate cell.
The skeleton code that accompanies this document already has most of the code for the assignment except for a few minor things, such as experiments with different features and generating the test accuracies and computing their mean. The latter, however, can easily be done following the code used for validation accuracy computation. Hence, the only challenge for you is to write code for testing with different features.
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