Training Data Alchemy: Balancing Quality and Quantity in Machine Learning Training

Abstract

Determining the optimal amount of training data for machine learning algorithms is a critical task in achieving successful and accurate models. This abstract delves into the research surrounding this question and provides insights into the factors that affect the quantity of training data required for effective machine learning. It explores the delicate balance between data quality and quantity, the concept of overfitting, and the importance of representative and diverse datasets. Additionally, it discusses the various techniques and approaches used to estimate the minimum training data required for achieving desirable performance. By understanding the implications of training data size on model performance, researchers and practitioners can make informed decisions in selecting appropriate training datasets, thereby maximizing the efficiency and effectiveness of machine learning algorithms.