Probability is how strongly we believe that an event will happen, mostly expressed in percentage.
These are some of the examples or questions that you can ask as a part of Probability
- How likely will i get 8 heads in 10 fair flip coins?
- What are my chances of wining a lottery?
- Will my flight be late?
- How certain I am that a product is defective?
Like I said, most of the time the Probability is expressed in percentage.
There is a 70% chance that it will rain or there is 90% chance that my flight will be delayed
We will call this P(X) where X is the event of interest.
As we will work more with probabilities, we will see that the probability is expressed as decimal, in the above case it .70, and it must be between 0.0 and 1.0
So we can say P(X) = .70
Some people interchangeably use likelihood and probability. But here, probability is more about quantifying events that are yet to happen. Where as “likelihood” is more about measuring the frequency of events that has already happened.
In Machine Learning and Statistics, likelihood is the data that we used to predict the probability.
Now one of the most important point here to remember, the probability of an event must be between 0%-100% or 0.0 and 1.0.
Logically this means, the probability of an event not happening is substractiing the event from 1.0
P(X)=0.70
P(not X)=1-.70 = .30
