eMLEE

Enhanced Machine Learning Engine Engineering (eMLEE) Using Innovative Blending, Tuning, and Feature Optimization Fahim Uddin, Ph.D  Machine Learning is more art than science.  Let us optimize this art through science. Investigated into and motivated by Ensemble Machine Learning (ML) techniques, this thesis contributes to addressing performance, consistency, and integrity issues such as overfitting, underfitting, predictive errors, accuracy paradox, and poor generalization for the ML models. Ensemble ML methods have shown promising outcome when a single algorithm failed to approximate the true prediction function. Using meta-learning, a super learner is engineered by combining weak learners.  Generally, several algorithms in Supervised Learning (SL) are evaluated to find the best fit to the underlying data and predictive analytics (i.e., “No Free Lunch” Theorem relevance). This thesis addresses three main challenges/problems, i) determining the optimum blend ...