Machine Learning - Probability and Counting Rules - Empirical Probability

In this video we are going to discuss empirical probability

The difference between the classical and empirical probability is that the classical

probability assumes that certain outcomes are equally likely. That means each and every outcome is equally likely that was considered in our respective

classical probability. You can watch the previous video where you have discussed this

Such as the outcomes when it died, is rolled while empirical probability relies on actual experience to determine the likelihood

of outcomes. So, in empirical probability one might actually roll a die, say 6,000 times observe the various

frequencies and use these frequencies to determine the probability of an outcome

So here a die will be rolled for 6,000 number of times, might be also 10,000 number of times

And for each and every phase value that is 1 2 3 4 5 6 it will go on counting the number of frequencies and from there it will try to calculate the frequency or the probability of occurrence of a certain event So just consider this one drive 41 is the frequency fly 6 is the frequency and

train or bus, 3 is the frequency. So in total, the value is 50

For example, the probability of selecting a person who is driving is 41 by 50

So in this way, because here we are having this drive, drive is having the frequency, 41

the sum of all frequencies is 50. So, the probability that person will drive will be 41 by 50

So in this way, the empirical probability can be defined in this example

So formula for empirical probability. Given a frequency distribution, the probability of an event being in a given class is

so that is the probability of the event E is equal to frequency of the class by total frequencies

in the distribution. So, that is small f by small n. This probability is called empirical probability and is based on the observation

I think you are getting this issue. What is empirical probability and also we have explained this concept with some examples

Thanks for watching this video