Databricks Databricks-Certified-Professional-Data-Scientist Exam With Confidence Using Practice Dumps

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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Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.