Wide model — это линейная модель машинного обучения со множеством sparse input features, напрямую connected to output. Ее часто проще inspect и debug, чем deep model, при этом она все еще может capture some nonlinear relationships через transformations, такие как feature crossing или 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.