A wide model is a linear machine learning model with many sparse input features connected directly to the output. It is often easier to inspect and debug than a deep model, while still being able to capture some nonlinear relationships through transformations such as feature crossing or bucketization.
A linear model that typically has many sparse input features. We refer to it as "wide" since such a model is a special type of neural network with a large number of inputs that connect directly to the output node. Wide models are often easier to debug and inspect than deep models. Although wide models cannot express nonlinearities through hidden layers, wide models can use transformations such as feature crossing and bucketization to model nonlinearities in different ways. Contrast with deep model.