TreeNet is a machine learning technique called stochastic gradient boosting developed by Jerome Friedman. It builds decision tree models in a stage-wise fashion, with each subsequent tree attempting to correct the errors of previous trees, resulting in a very accurate predictive model. TreeNet can handle both classification and regression problems, and has advantages such as being able to capture complex variable interactions and resist overfitting. It provides useful outputs for interpreting models such as variable importance rankings and partial dependency plots.