Is naive Bayes a generative or a discriminative model?
Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.
Is Hmm generative or discriminative?
CRF is known to be a discriminative model and HMM is a generative model. I had to refresh my knowledge about this categorisation of supervised machine learning methods, especially generative models.
Which is better generative or discriminative machine learning?
Generative models are computationally expensive compared to discriminative models. Generative models are useful for unsupervised machine learning tasks. Generative models are impacted by the presence of outliers more than discriminative models. Discriminative models model the decision boundary for the dataset classes.
Which is less accurate generative or discriminative model?
Generative models are less accurate than discriminative models when the assumption of conditional independence is not satisfied. For example, in our spam classification problem, let x1 = number of times “bank” appear in the email data, and x2 = number of times “account” appear in the email.
How are generative and discriminative models used in PGMs?
To get the conditional probability P (Y|X), generative models estimate the prior P (Y) and likelihood P (X|Y) from training data and use Bayes rule to calculate the posterior P (Y |X): On the other hand, discriminative models directly assume functional form for P (Y|X) and estimate parameters of P (Y|X) directly from training data.
Where does discriminative model fall in supervised learning?
The discriminative model falls under the supervised learning branch. In a classification task, given that the data is labelled, it tries to distinguish among classes, for example, a car, traffic light and a truck.