Exploring the World of Probability Theory in ML
* What is Probability and how
can it be used? Probability is the likelihood of an event which means that
Probability can help someone to determine the possibility of something to
happen or not using the mathematical (Gannita
Gyaana) where one can establish the possibility or likelihood of occurrence
of an event in terms with the total number of possible events that could likely
occur .
* The probability of an event is
measured in the range from 0 (no probability that an event occurs) to the value
of 1 ( a certainty that an event occurs ) which in relative terms says about
the extent of any value towards the any of the extremes from the left most to
the right most values .
* The probability of picking a
certain suit from a deck of Cards (generally referred to as "Taash" in many Asian countries)
is one of the most classic example on explanation of probabilities.
* The deck of cards contains 52
cards (joker cards excluded) which can be divided into four suits as clubs and
spades which are black , and diamonds and hearts which are red in colour .
* Therefore , if one wants to
determine whether the probability of picking the card is an ace , then one must
consider that there are four aces of different suits .The probability of such
an event can be calculated as p = 4/52 which is again evaluated to 0.077.
* Probabilities are between the
values of 0 and 1 ; no probability can exceed such boundaries as everything's
possibility of occurrence lies between nothing to everything and probability of
not occurrence of something is always zero and the probability of occurrence of
everything is always equal to 1 .
* If someone tries to do a
Probability Possibility prediction for a given case of fraud detection in which
one would like to see and find out the number of times a bank transaction
related fraud has occurred over a given set of bank accounts or how many times
fraud happens while conducting a banking transaction or how many times people
get a certain disease in a particular country . So , after associating all the
events , one can estimate the probability of occurrence of associating all the
events , one can estimate the probability of occurrence of such forthcoming
event with regards to the frequency of occurrence , mode of occurrence , time
of occurrence , as well as the likely accounts which could be affected by the
fraud and the conditions which are likely to affect the accounts .
The calculation for the estimation
would take into consideration of counting the number of times a particular
event occured and dividing the total number of events that could possibly occur
for a set of operations and calculations.
* One can count the number of
times the fraud happens using recorded data ( which are mostly taken from
databases ) and then one would divide that figure by the total number of
generic events or observations available
* Therefore , one should divide
the total number of frauds by the number of transactions within a year or one
can count the total number of people who fell ill during the year with respect
to the population of a certain area . The result of this is a number ranging
from 0 to 1 which one can use as baseline probability for a certain event under
certain type of circumstances
* Counting all the occurrences
of an event is not always possible for which one needs to know about the
concept of sampling. Sampling is an act which is based on certain probability
of expectations , which one can observe as a small part of a larger set of
events or objects , yet one may not be able to infer correct probabilities for
an event , as well as exact measures such as quantitative measurements or
qualitative classes related to a set of objects
* Example - If one wants to
track the sales of cars in a certain country , then one doesn't need to track
all the sales that occur in that particular geography ... rather using a sample
comprising of all the sales from new car sellers around the country , one can
determine the quantitative measures such as average price of a car sold or
qualitative measures such as the car model which were sold most often
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