How Gaussian distribution is used in machine learning?

Normal Distribution is an important concept in statistics and the backbone of Machine Learning. As discovered by Carl Friedrich Gauss, Normal Distribution/Gaussian Distribution is a continuous probability distribution. It has a bell-shaped curve that is symmetrical from the mean point to both halves of the curve.

What is Gaussian SVM?

Gaussian RBF(Radial Basis Function) is another popular Kernel method used in SVM models for more. RBF kernel is a function whose value depends on the distance from the origin or from some point. Gaussian Kernel is of the following format; ||X1 — X2 || = Euclidean distance between X1 & X2.

Why is Gaussian distribution important in machine learning?

Why is Gaussian Distribution Important? Gaussian distribution is the most important probability distribution in statistics because it fits many natural phenomena like age, height, test-scores, IQ scores, sum of the rolls of two dices and so on.

Why do we use Gaussian kernel?

Gaussian kernels are universal kernels i.e. their use with appropriate regularization guarantees a globally optimal predictor which minimizes both the estimation and approximation errors of a classifier. Gaussian kernels are circular (which leads to the above-mentioned infinite dimensionality?)

What is the use of Gaussian distribution?

A sample of data will form a distribution, and by far the most well-known distribution is the Gaussian distribution, often called the Normal distribution. The distribution provides a parameterized mathematical function that can be used to calculate the probability for any individual observation from the sample space.

What is Gaussian distribution in data science?

Gaussian distribution is a continuous probability distribution with symmetrical sides around its center. Its mean, median and mode are equal. Its shape looks like below with most of the data points clustered around the mean with asymptotic tails.

How does Gaussian kernel work?

In other words, the Gaussian kernel transforms the dot product in the infinite dimensional space into the Gaussian function of the distance between points in the data space: If two points in the data space are nearby then the angle between the vectors that represent them in the kernel space will be small.

What are Gaussian models used for?

Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal distribution. For example, if a random process is modelled as a Gaussian process, the distributions of various derived quantities can be obtained explicitly.