  Dedicated neural network (NN) architectures have been designed to handle
specific data types (such as CNN for images or RNN for text), which ranks them
among state-of-the-art methods for dealing with these data. Unfortunately, no
architecture has been found for dealing with tabular data yet, for which tree
ensemble methods (tree boosting, random forests) usually show the best
predictive performances. In this work, we propose a new sparse initialization
technique for (potentially deep) multilayer perceptrons (MLP): we first train a
tree-based procedure to detect feature interactions and use the resulting
information to initialize the network, which is subsequently trained via
standard stochastic gradient strategies. Numerical experiments on several
tabular data sets show that this new, simple and easy-to-use method is a solid
concurrent, both in terms of generalization capacity and computation time, to
default MLP initialization and even to existing complex deep learning
solutions. In fact, this wise MLP initialization raises the resulting NN
methods to the level of a valid competitor to gradient boosting when dealing
with tabular data. Besides, such initializations are able to preserve the
sparsity of weights introduced in the first layers of the network through
training. This fact suggests that this new initializer operates an implicit
regularization during the NN training, and emphasizes that the first layers act
as a sparse feature extractor (as for convolutional layers in CNN).
