we generate 6 sets of datasets from MNIST that consists of balanced dataset, imbalanced dataset, balanced dataset with symmetric noise, balanced dataset with asymmetric noise, imbalanced dataset with symmetric noise and imbalanced dataset with asymmetric noise.
implementation of random forest and logistic regression models with evaluation metrics.
implementation of LDAM-CRW model and Symmetric Cross Entropy loss
Combination of UMAP with Random Forest as proposed ML model that improves RF baseline and implementation of focal loss within Symmetric Cross Entropy loss to improve DL baseline.