What is sampling in #MachineLearning and what are different sampling techniques?
Detailed analysis of 10 widely used sampling techniques. (Notes at the end π)
A thread π§΅
PS: There is a Notion document at the end of the thread with detailed notes on this topic π
Detailed analysis of 10 widely used sampling techniques. (Notes at the end π)
A thread π§΅
PS: There is a Notion document at the end of the thread with detailed notes on this topic π
Population vs Sample β¨
π Population - Population is the collection of the elements which has some or the other characteristic in common.
π Sample - Sample is the subset of the population. The process of selecting a sample is known as sampling
π Population - Population is the collection of the elements which has some or the other characteristic in common.
π Sample - Sample is the subset of the population. The process of selecting a sample is known as sampling
Why do we even need sampling π€?
π Dealing with a complete population is very hard (almost impossible). Sampling is a method that allows us to get information about the population without investigating every individual.
π Dealing with a complete population is very hard (almost impossible). Sampling is a method that allows us to get information about the population without investigating every individual.
There are two main types of sampling:
1) Probability Sampling - uses randomization to make sure that every element of the population gets an equal chance to be part of the selected sample.
1) Probability Sampling - uses randomization to make sure that every element of the population gets an equal chance to be part of the selected sample.
2) Non-Probability sampling - doesn't rely on randomization, more reliant on the researcher's ability to select elements, which may introduce selection bias
Probability Sampling is further divided into:
1) Simple Random Sampling
2) Stratified Sampling
3) Cluster Sampling
4) Systematic Sampling
5) Multistage Sampling
1) Simple Random Sampling
2) Stratified Sampling
3) Cluster Sampling
4) Systematic Sampling
5) Multistage Sampling
Non-Probability Sampling is further divided into:
1) Convenience Sampling
2) Voluntary Sampling
3) Snowball Sampling
4) Purposive Sampling
5) Quota Sampling
1) Convenience Sampling
2) Voluntary Sampling
3) Snowball Sampling
4) Purposive Sampling
5) Quota Sampling
It is difficult to explain each of these sampling techniques in this thread π, so I have made a Notion page explaining each of them in detail (its applications, advantages, disadvantages, with code) π
πNotes:
atharvaingle.notion.site/Sampling-Technβ¦
atharvaingle.notion.site/Sampling-Technβ¦
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