Random Variable
We learn complex algorithm of AI/ML and try to find out the solution about a problem related to vision, deep learning, regression and forest tree etc. But what exactly we are finding?
Actually, we are looking for a quantitative value that gives us the probabilities of the output of an event. True?
Which is called a “Random Variable”.
“The Random Variable is a number that quantifies the outcome of an event in terms of probability”.
By the definition the Random Variable can be Discrete and Continuous.
There are X number of people visit a shop in a given day. So how many people will buy something from the shop?
This can be any number from 0 -> X.
X/2 people will buy and X/2 people will not buy.
0 will buy X will not buy
or X will by 0 will not buy.
This can be any number between 0 to X. There is no certain number we can say how many going to buy from the shop. This randomness is capture in Random Variable.
The Random Variable is a value associated with every person enters into shop and in totality we called the value of Random Variable.
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The Random Variable can be different type depends upon what type of event or what we choose to measure.
Discrete —
This is simple to describe and counting type of variable.
From above example, lets assume total 100 people goes to shop—
Random Variable ( RV) value can be —
RV = 20 people will buy with probability 0.80
+ 35 people will buy with probability 0.12
+ 45 people will buy with probability 0.08
RV takes possible values 20, 35, 45 with respective probability 0.80, 0.12 and 0.08.
So the RV values is distributed with 4 different possible outcome and total is 1.
Continuous-
This type of Random Variable is the variable which hold the values in innumerous way which can not be count. Like Co2 quantity in the air. This can not be measure by group of numbers like 5 ppm or 10 ppm this can be anything . To measure we need to use terms like more then greater then or less or more.
Co2 in air is > 400 ppm and less then 600 ppm is good.
This gives us an idea of distribution, which I will describe in another post.
