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What is the other name of Gaussian distribution?
Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.
Which classification algorithm is used when there is a case of continuous distribution characterized by its mean and variance?
Naive Bayes in scikit-learn The second one is a discrete distribution used whenever a feature must be represented by a whole number (for example, in natural language processing, it can be the frequency of a term), while the latter is a continuous distribution characterized by its mean and variance.
How do you know what type of distribution to use?
Using Probability Plots to Identify the Distribution of Your Data. Probability plots might be the best way to determine whether your data follow a particular distribution. If your data follow the straight line on the graph, the distribution fits your data. This process is simple to do visually.
How is Gaussian discriminant analysis used in classification?
Both Logistic Regression and Gaussian Discriminant Analysis used for classification and both will give a slight different Decision Boundaries so which one to use and when. GDA makes an assumption about the probability distribution of the p (x|y=k) where k is one of the classes.
How is a Gaussian classifier a generative approach?
A Gaussian classifier is a generative approach in the sense that it attempts to model class posterior as well as input class-conditional distribution. Therefore, we can generate new samples in input space with a Gaussian classifier.
How is the EM algorithm used in Gaussian mixture model?
There comes the EM algorithm. Let see how the EM algorithm is used in the Gaussian mixture model. Maximum Likelihood Estimation (MLE) can be simplified by introducing the Latent variable. A latent variable model makes the assumption that an observation xi is caused by some underlying latent variable.
Can a Gaussian mixture model be used to cluster data?
Gaussian mixture models can be used to cluster unlabeled data in much the same way as k-means. There are, however, a couple of advantages to using Gaussian mixture models over k-means. First and foremost, k-means does not account for variance.
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