📌 Weighted Sampling Summary

Weighted sampling is a method for selecting items from a group where some items are given a higher chance of being chosen than others. Each item is assigned a weight, which indicates its importance or likelihood of selection. This approach is often used when some options are more relevant or common than others, so the sample better reflects real-world proportions.

🙋🏻‍♂️ Explain Weighted Sampling Simply

Imagine you have a bag of sweets, but some types are more popular than others. If you want to pick a sweet at random but still make sure the favourites are chosen more often, you would put more of those in the bag. Weighted sampling works like that, making sure the more important or frequent options get picked more often.

📅 How Can it be used?

Weighted sampling can help a recommendation system suggest products that are both popular and diverse among users.

🗺️ Real World Examples

An online retailer uses weighted sampling to pick which products to show on its homepage. Products with higher sales or better customer ratings are given more weight, so they are more likely to appear in the featured section, while still giving a chance to less popular items.

In medical research, scientists may use weighted sampling when selecting participants from different age groups. If older adults are less common in the population but need to be represented, their selection weight can be increased to ensure they are included appropriately in the study.

✅ FAQ

Weighted sampling is a way to choose items from a group so that some have a bigger chance of being picked than others. This is helpful when some items are more important or more common, so the sample gives a better reflection of what you would see in real life.

You might come across weighted sampling in surveys where some groups of people are more represented, or in music playlists where popular songs are played more often. It helps make sure the results or choices match real-world popularity or importance.

Weights are usually given based on how important or common each item is. For example, if one choice should appear twice as often as another, it will be given a higher weight to reflect its bigger role or likelihood.

📚 Categories

• Data Science
• Model Optimisation Techniques
• Model Training & Tuning

🔗 External Reference Links

Weighted Sampling link

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