  A new ensemble framework for interpretable model called Linear Iterative
Feature Embedding (LIFE) has been developed to achieve high prediction
accuracy, easy interpretation and efficient computation simultaneously. The
LIFE algorithm is able to fit a wide single-hidden-layer neural network (NN)
accurately with three steps: defining the subsets of a dataset by the linear
projections of neural nodes, creating the features from multiple narrow
single-hidden-layer NNs trained on the different subsets of the data, combining
the features with a linear model. The theoretical rationale behind LIFE is also
provided by the connection to the loss ambiguity decomposition of stack
ensemble methods. Both simulation and empirical experiments confirm that LIFE
consistently outperforms directly trained single-hidden-layer NNs and also
outperforms many other benchmark models, including multi-layers Feed Forward
Neural Network (FFNN), Xgboost, and Random Forest (RF) in many experiments. As
a wide single-hidden-layer NN, LIFE is intrinsically interpretable. Meanwhile,
both variable importance and global main and interaction effects can be easily
created and visualized. In addition, the parallel nature of the base learner
building makes LIFE computationally efficient by leveraging parallel computing.
