This session is about population was the sample, and what we are learning here will form the basis
for the next session.
It is hypothesis testing.
So let's understand this concept clearly.
When you're working on a mission learning project, you don't get to work on the complete data set.
It could be due to various reasons like buying cost.
The space may not be available.
Part of the data may even be corrupted or any other reasons.
So you get to work on your part of the complete data set known as a sample when you're working on a
sample.
What we're actually doing is you're trying to draw inferences for the population based on the inferences
of the sample.
Right.
That's what you're doing, your developing machine learning model for your smaller data set, but you
apply that machine learning model for the entire dataset.
This population was a sample place in our day to day life also.
Let's say you're trying to admit your kid in a school and let's say the school has got to find its students
before admitting your kid.
You may probably want to take feedback about the school from 20 students whom you already know.
It is practically not possible to collect feedback from 500 students, but as it is possible to collect
feedback from 20 students, if you see here, if all the grindy students give a good feedback out of
the majority of the students, give a good feedback, I may probably end up admitting my kid in that
particular school.
So what am I doing here?
I am extrapolating the inference that I got from my sample on to the entire population.
I'm saying whatever inference are drawn for the sample is applicable for the population as well.
Right now, to ensure that whatever I have drawn as insurance, for example, is applicable for the
population, I must ensure that this sample is representative.
So what is this representativeness if you see this example that is there on your screen?
There are different types of students in the population and there are different types of students in
the sample.
OK, now if the percentage of different types of students is maintained in the sample, also, if the
same level of percentage is maintained in my sample, then the sample is said to be representative of
the population.
In this scenario, this 43, 22, 17 and 17 is not reflected in the sample, the percentage contribution
of different types of students is different from the members or the percentage that you see here.
Hence, the sample is not representative.
There's a very, very important concept in machine learning or deploying.
Why do you think this is applicable, reflecting your developing a machine learning model to predict
how diseases and if the majority of your dataset comprises of Indians and you go and apply the model
that you've developed with the majority of Indians in your database to, let's say, Chinese or Caucasians,
your inference wouldn't be accurate enough because the propensity for heart disease is much higher in
Indians and Asians compared to Chinese and Caucasians.
Right, your model will be accurate onely, if the percentage contribution of different races is addressed
in your sample.
Are you understanding why this concept is relevant when it comes to machine learning and deep learning?
OK.
The same concept of representative sampling is important in the world of opinion polls, also, if let's
say you're trying to assess the likelihood of a particular party winning the elections and see us or
India, you must ensure that your sample is representative of the population of the electorate.
If you're going to collect more feedback from, say, northern or central India, your conclusion or
inference won't be comprehensive or accurate.
The same holds true for the US also.
If your feedback is primarily from coastal areas, right, and you ignore subtle and mysterious, your
inference wouldn't be accurate because your sample is not representative of the population of electorates.
So please keep this in mind whenever you're working on samples.
The other aspect when it comes to sampling is what is known as random sampling.
Random sampling ensures that there are no biases when it comes to picking the sample for taking feedback
or doing any type of study in the example that you see on your screen, that is one a local or one red
color one, one blue color one and two green colored balls.
Now, the blue colored ball, I have taken the ball that is number six instead of six.
I could have picked up an eight or 11, nine, 14 or 16 or 17.
Each of these blue balls have an equal chance of being picked up the sample.
If I ensure that each of the blue balls have an equal chance of being picked up, then I have short
randomness in the sampling.
Now, let's try to understand this, using the school example, there are 500 students and I am taking
feedback from 20 students.
What if I collect feedback from 20 students?
I know very well I'm introducing a bias, right.
So when it comes to sampling, let's ensure the randomness is maintained and representativeness is also
maintained, only then the model that you develop will be accurate, otherwise you will have lower accuracy.
OK.