Fig. 1
From: Predicting cash holdings using supervised machine learning algorithms
The representation of a multi-layer neural network (Dixon et al. 2017). The input layer consists of explanatory variables called features, and the information is forwarded from this layer to the hidden layers. On the arcs of hidden layers, parameters called weights and biases exist. The goal of the network is to find the optimal parameter settings that minimize the error between the estimated and the actual target value