Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach
Fig 5
A SHAP force plot of a single individual.
This method examines the factors that influenced the model for prediction on a single individual, showing questions that led the model to think they are more likely to have STB in red and questions that led the model to think they are less likely to have STB in blue. A decision boundary of more than zero indicates that the model predicts that they have STB. In this example, you can see that their answer to Q138 of 5 (frequent internet harassment), their answer to Q25C of 6 (early alcohol usage), and their answer to Q38H of 2 (violent activity) led the model to predict that they have STB. This allows for easy interpretability of the model’s results, making it more trustworthy and transparent.