Conditionalism as a Methodology of Forecasting, Decision-Making and Machine Learning

We examine how conditionality inherent to the real-life training data sets influences forecasting and decision-making by analyzing examples that range from behavioral logic machine learning. For learning or other systematic methods, we outline a process-driven approach better navigate improve decisions in situations with obscure difficult capture conditionality. implementation of these techniques portfolio management engine.