Discriminative vs. Generative

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Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model. Model Contrasts...

• Discriminative
  • learn the (hard or soft) boundary between classes
  • providing classification splits (probabilistic or non-probabilistic manner)
  • allow you to classify points, without providing a model of how the points are actually generated
  • don't have generative properties
  • make few assumptions of the model structure
  • less tied to a particular structure
  • better performance with lots of example data; higher accuracy, which mostly leads to better learning result
  • discriminative models can yield superior performance (in part because they have fewer variables to compute)
  • saves calculation resource
  • can outperform generative if assumptions are not satisfied (real world is messy and assumptions are rarely perfectly satisfied)
  • not designed to use unlabeled data; are inherently supervised and cannot easily support unsupervised learning
  • do not generally function for outlier detection
  • do not offer such clear representations of relations between features and classes in the dataset
  • yields representations of boundaries (more than generative)
  • do not allow one to generate samples from the joint distribution of observed and target variables
  • generates lower asymptotic errors

• Generative
  • requires less training samples
  • model the distribution of individual classes
  • provides a model of how the data is actually generated
  • learn the underlying structure of the data
  • have discriminative properties
  • make some kind of structure assumptions on your model
  • decision boundary: where one model becomes more likely
  • often outperform discriminative models on smaller datasets because their generative assumptions place some structure on your model that prevent overfitting
  • natural use of unlabeled data
  • takes all data into consideration, which could result in slower processing as a disadvantage
  • generally function for outlier detection
  • typically specified as probabilistic graphical models, which offer rich representations of the independence relations in the dataset
  • more straightforward to detect distribution changes and update a generative model
  • takes the joint probability and predicts the most possible known label
  • typically more flexible in expressing dependencies in complex learning tasks
  • a flexible framework that could easily cooperate with other needs of the application
  • results in higher asymptotic errors faster
  • training method usually requires multiple numerical optimization techniques
  • will need the combination of multiple subtasks for a solving complex real-world problem

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