Sampling

• Two families of sampling
  • nonprobability sampling
  • random sampling (probability based sampling)
• Nonprobability sampling
  • convenience sampling - selection based on availability
  • snowball sampling - Future samples selected based on exisitng samples
  • Judgment sampling
  • Quota sampling - You select samples based on quotas for certain slices of data without any randomization
• Random sampling
  • simple random sampling - All samples in the population equal probability of being selected
  • Stratified sampling - to sample 1% of data that has two classes, A and B, you can sample 1% of class A and 1% of class B. Challenging in case of multilabel tasks
• Weighted sampling
  • In weighted sampling, each sample is given a weight, which determines the probability of it being selected.

# Choose two items from the list such that 1, 2, 3, 4 each has
# 20% chance of being selected, while 100 and 1000 each have only 10% chance.
import random
random.choices(population=[1, 2, 3, 4, 100, 1000],
               weights=[0.2, 0.2, 0.2, 0.2, 0.1, 0.1],
               k=2)
# This is equivalent to the following
random.choices(population=[1, 1, 2, 2, 3, 3, 4, 4, 100, 1000],
               k=2)

• Reservoir sampling - useful when have to deal with streaming data

Importance sampling