Neural networks and random forests are popular and promising tools for machine learning. We explore the proper integration of these two approaches for nonparametric regression to improve the performance of a single approach. It naturally synthesizes the local relation adaptivity of random forests and the strong global approximation ability of neural networks.. By utilizing advanced U-process theory and an appropriate network structure, we obtain the minimax convergence rate for the estimator. Moreover, we propose the novel random forest weighted local Frechetregression paradigm for regression with Non-Euclidean responses. We establish the consistency, rate of convergence, and asymptotic normality for the Non-Euclidean random forests based estimator.
