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# Which of the following is a discriminative classifier?

## Which of the following is a discriminative classifier?

Discriminative Classifiers learn what the features in the input are most useful to distinguish between the various possible classes. An example of a discriminative classifier is logistic regression. Mathematically, it directly calculates the posterior probability P(y|x) or learn a direct map from input x to label y.

## What is discriminative probabilistic model?

The discriminative model is used particularly for supervised machine learning. Also called a conditional model, it learns the boundaries between classes or labels in a dataset. It creates new instances using probability estimates and maximum likelihood.

## Which algorithm is discriminative in nature?

Logistic regression, SVM, and tree based classifiers (e.g. decision tree) are examples of discriminative classifiers. A discriminative model directly learns the conditional probability distribution P(y|x).

## Is GMM generative or discriminative?

Generative / nonparametric: GMM which learns Gaussian distribution and have unfixed amount of parameters (latent parameters increases depending on the sample size) Generative / parametric: various Bayes based model. Discriminative / parametric: GLM, LDA and logistic regression.

## Is Lstm discriminative or generative?

Employing the long short-term memory (LSTM) structure, we develop a discriminative model based on the hidden state and a generative model based on the cell state.

## Is K means discriminative or generative?

It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised …

## Is naive Bayes discriminative or generative?

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.

## What is difference between generative and discriminative model?

Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.

## How is a discriminative model used in regression?

Discriminative model. Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick.

## How are discriminative models used in machine learning?

Discriminative models, also referred to as conditional models or backward models, are a class of supervised machine learning used for classification or regression. These distinguish decision boundaries by inferring knowledge from observed data.

## How are discriminative models used in data mining?

data mining. Discriminative models, also referred to as conditional models, are a class of models used in statistical classification, especially in supervised machine learning. A discriminative classifier tries to model by just depending on the observed data while learning how to do the classification from the given statistics.

## How is a conditional model different from a discriminative model?

A conditional model models the conditional probability distribution, while the traditional discriminative model aims to optimize on mapping the input around the most similar trained samples. . to simulate the behavior of what we observed from the training data-set by the linear classifier method. Using the joint feature vector . Then the