A classification model that uses a machine learning to categorize an image of a shoe as either a sneaker or a sandal. The app converts images into a 2D array of pixels (28x28) which are classified by a support vector machine (SVM) powered by a Gaussian (RBF) kernel.
- The model's parameters are the regularization parameter
C = 4and the kernel coefficientγ = 0.036. These were the results of hyperparameter tuning with seven-fold cross-validation. - The training set had 4000 images, and the test set had 667 (6:1 split). The model ultimately performed with 98.4% accuracy based on the data.
- The 95% confidence interval for the test error is
(0, 0.0687).
Requirements are cataloged in requirements.txt.
The classifier can be run from the command line with make, which will run the site from localhost:5000.
- Model trained using the Fashion-NMIST dataset.
- Implemented with the scikit-learn machine learning package for Python