  In this research. we analyze the potential of Feature Density (HD) as a way
to comparatively estimate machine learning (ML) classifier performance prior to
training. The goal of the study is to aid in solving the problem of
resource-intensive training of ML models which is becoming a serious issue due
to continuously increasing dataset sizes and the ever rising popularity of Deep
Neural Networks (DNN). The issue of constantly increasing demands for more
powerful computational resources is also affecting the environment, as training
large-scale ML models are causing alarmingly-growing amounts of CO2, emissions.
Our approach 1s to optimize the resource-intensive training of ML models for
Natural Language Processing to reduce the number of required experiments
iterations. We expand on previous attempts on improving classifier training
efficiency with FD while also providing an insight to the effectiveness of
various linguistically-backed feature preprocessing methods for dialog
classification, specifically cyberbullying detection.
