New Release Databricks-Certified-Professional-Data-Scientist Databricks Certification Questions

In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.

In this problem you have been given high-dimensional independent variables like yes, no: test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.

Support vector machines Naive Bayes Logistic regression Random decision forests

Given two random variables X and Y whose joint distribution is known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y. It is the probability distribution of X when the value of Y is not known. This is typically calculated by summing or integrating the joint probability distribution over Y. '

For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is

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where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out.

Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables.

Similarly for continuous random variables, the marginal probability density function

can be written as pX(x). This is

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where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the

conditional distribution for X given Y Again: the variable Y has been marginalized

out.

Note that a marginal probability can always be written as an expected value:

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Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general

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Explanation :

The difference between their properties can be promptly summarized as follows:

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