Improving Performance of Supervised Machine Learning Algorithms on Small Datasets

In most cases, machine learning involves working with large volumes of data in order to perform optimally. However, in some cases, there is only a limited amount of training data available, resulting in a decrease in the performance of the model. The current research aims to enhance the effectiveness of machine learning models, which have been developed utilizing limited services. In this case study, the focus is on Learning using a multi-iteration approach along with Gaussian Mixture Model GMM-based Synthetic Data Generation, which were suggested as solutions to the problem of data scarcity. The research explores how much these methods boost a model's performance in the performance metrics