MNIST-Analysis

K-fold cross-validation implemented from scratch to aid in analysis of the MNIST dataset
It was determined that the left side of the numbers 3 and 8 are the important features used in differentiating between handwriting samples of the numbers 3 and 8. In the Hard2ClassifyData folders, we can see that samples with light handwriting are typically hard to classify as well as "scrunched" handwriting, where the lower part of 3 overextends to almost make it look like an 8. An example is below:

Results

Important pixels to differentiate between a 3 and an 8 as determined by Logistic Regression (brighter pixels means more important)

Important pixels to differentiate between a 3 and an 8 as determined by Linear SVM (brighter pixels means more important)

The folders Easy2ClassifyData[MLmodel] and Hard2ClassifyData[MLmodel] contain examples of handwriting that is easy for the ML model to classify and hard for the ML model to classify, respectively.

To run

1. Unzip the MNIST.zip file to obtain the MNIST dataset. Ensure the unzipped folder remains in the same folder as the file MNIST.zip.
2. To replicate the results, simply open a file and run.
   • cv_builtin.py will fit a Logistic Regression and Linear SVM model to the data and print the tuned hyperparameters as determined by scikit-learn methods.
   • cv_scratch.py will run the cross validation implemented from scratch. Feel free to change the number of folds K, which is defined as a global variable to experiment with the method.
   • important_features.py will use the best lambda (can be changed in the code) as determined by cv_scratch.py to find the pixels in an image that are important in differentiating between a 3 and an 8.