The article introduces a new method to measure the relationship between a group of factors and a complex outcome. Traditional methods work well for simple relationships, but this new approach can handle more complicated connections. By using machine learning, the researchers can detect and measure nonlinear relationships and interactions between factors. They found that their method is more powerful in detecting associations compared to existing techniques, especially when factors have nonlinear relationships with outcomes. The researchers also developed a way to summarize the importance of groups of factors in relation to the outcome. They tested their method using data from a study on childhood health and nutrition in the Philippines.