Machine learning has been a subject undergoing intense study across many different industries and academic research areas. Companies and researchers have taken full advantages of various machine learning approaches to solve their problems; however, vast understanding and study of the field is required for developers to fully harvest the potential of different machine learning models and to achieve efficient results. Therefore, this thesis begins by comparing auto ml with other hyper-parameter optimization techniques. auto ml is a fully autonomous framework that lessens the knowledge prerequisite to accomplish complicated machine learning tasks. The auto ml framework automatically selects the best features from a given data set and chooses the best model to fit and predict the data. Through multiple tests, auto ml outperforms MLP and other similar frameworks in various datasets using small amount of processing time.

The thesis then proposes and implements a stacking ensemble technique in order to build protection against over-fitting for small datasets into the auto ml framework. Stacking is a technique used to combine a collection of Machine Learning models’ predictions to arrive at a final prediction. The stacked auto ml ensemble results are more stable and consistent than the original framework; across different training sizes of all analyzed small datasets.