Sage-Tips

What is the main difference between generative and discriminative classifiers?

Another key difference between these two types of models is that while a generative model focuses on explaining how the data was generated, a discriminative model focuses on predicting labels of the data.

What is the difference between a generative model and a discriminative model give examples of both types of models?

In simple words, a discriminative model makes predictions based on conditional probability and is either used for classification or regression. On the other hand, a generative model revolves around the distribution of a dataset to return a probability for a given example.

What are generative and discriminative algorithms?

Generative models are a wide class of machine learning algorithms which make predictions by modelling joint distribution P(y, x). Discriminative models are a class of supervised machine learning models which make predictions by estimating conditional probability P(y|x).

What is generative learning?

Abstract. Generative Learning Theory (GLT) suggests that learning occurs when learners are both physically and cognitively active in organizing and integrating new information into their existing knowledge structures.

What is a generative model in machine learning?

Generative modeling is used in unsupervised machine learning as a means to describe phenomena in data, enabling computers to understand the real world. This AI understanding can be used to predict all manner of probabilities on a subject from modeled data.

What is discriminative learning?

Discriminative learning refers to any classification learning process that classifies by using a model or estimate of the probability P(y\,\vert x) without reference to an explicit estimate of any of P(x), P(y, x), or P(x \vert \,y), where y is a class and x is a description of an object to be classified.

What is generative machine learning?

Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.

What are the advantages of discriminative classifier?

Takes all data into consideration, which could result in slower processing as a disadvantage. Requires fewer training samples. A flexible framework that could easily cooperate with other needs of the application.

Why is generative learning important?

Generative learning strategies require students to make sense of new information by selecting important information, reorganising and integrating the newly acquired information with what is already known. Therefore, use summarizing for aspects of the lessons where students are learning relatively simple concepts.

What is the difference between discriminative model and generative model?

In General, A Discriminative model ‌models the decision boundary between the classes. A Generative Model ‌explicitly models the actual distribution of each class. In final both of them is predicting the conditional probability P(Animal | Features).

What is generative model in machine learning?

Generative models are those that center on the distribution of the classes within the dataset. The machine learning algorithms typically model the distribution of the data points. Generative models rely on finding joint probability. Creating points where a given input feature and a desired output/label exist concurrently.

Why are discriminative models useful for supervised machine learning tasks?

Discriminative models are useful for supervised machine learning tasks. Discriminative models have the advantage of being more robust to outliers, unlike the generative models. Discriminative models are more robust to outliers compared to generative models.

What is the difference between discriminative and generative algorithms?

The distribution p(y|x)is the natural distribution for classifying a given example xinto a class y, which is why algorithms that model this directly are called discriminative algorithms. Generative algorithms model p(x,y), which can be transformed into p(y|x)by applying Bayes rule and then used for classification.