A Visual Guide To Sampling Techniques in Machine Learning

When you are dealing with large amounts of data, it is often preferred to draw a relatively smaller sample and train a model. But any mistakes can adversely affect the accuracy of your model.

This makes sampling a critical aspect of training ML models.

Here are a few popularly used techniques that one should know about:

🔹 Simple random sampling: Every data point has an equal probability of being selected in the sample.

🔹 Cluster sampling (single-stage): Divide the data into clusters and select a few entire clusters.

🔹 Cluster sampling (two-stage): Divide the data into clusters, select a few clusters, and choose points from them randomly.

🔹 Stratified sampling: Divide the data points into homogenous groups (based on age, gender, etc.), and select points randomly.

What are some other sampling techniques that you commonly resort to?

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