Contents

- 1 How does a discriminative model differ from a generative model?
- 2 What are the key differences between a generative model and a discriminative model from a statistical point of view?
- 3 Are generative models slower than discriminative models?
- 4 Is Random Forest generative or discriminative?
- 5 Is LDA a Bayesian?
- 6 Is LDA deep learning?
- 7 Are SVMs generative classifiers?
- 8 How are discriminative models different from generative models?
- 9 Which is better generative or discriminative machine learning?
- 10 Where does discriminative model fall in supervised learning?
- 11 How are generative models used in machine learning?

## How does a discriminative model differ from a generative model?

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 the key differences between a generative model and a discriminative model from a statistical point of view?

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.

## Are generative models slower than discriminative models?

The overall gist is that discriminative models generally outperform generative models in classification tasks. “which is why algorithms that model this directly are called discriminative algorithms”, still not sure why p(y|x) implies that algorithms that model it are called “discriminative models”.

## Is Random Forest generative or discriminative?

In other words, discriminative models are used to specify outputs based on inputs (by models such as Logistic regression, Neural networks and Random forests), while generative models generate both inputs and outputs (for example, by Hidden Markov model, Bayesian Networks and Gaussian mixture model).

## Is LDA a Bayesian?

LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation.

## Is LDA deep learning?

Deep learning technology employs the distribution of topics generated by LDA.

## Are SVMs generative classifiers?

Generative models such as HMMs and GMMs focus on estimating the density of the data and are not suitable for classifying the data of confusable classes. Discriminative classifiers such as support vector machines (SVM) are suitable for the fixed dimensional patterns.

## How are discriminative models different from generative models?

While generative models learn about the distribution of the dataset, discriminative models learn about the boundary between classes within a dataset. With discriminative models, the goal is to identify the decision boundary between classes to apply reliable class labels to data instances.

## 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.

## 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.

## How are generative models used 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.