Explain machine learning concepts in simple terms

Disclaimer

I use ChatGPT to create this ELI5 collection as a research into Human - Large Language Model collaboration. See methodology here.

I do not claim authorship nor guarantee the correctness of this article. However, I did put in efforts to make it correct and helpful in key aspects (see more in the methodology, link above).

less than 1 minute read

Variance in machine learning refers to the error that is introduced when a model’s predictions vary too much between different samples of data. For example, imagine you are trying to predict how much a person weighs based on their height. If your model’s predictions vary a lot depending on the specific group of people you are using to train the model, it would have high variance.

High variance can be a problem in machine learning because it can lead to unstable and unreliable models. For example, if a model has high variance, it might make very different predictions for two people who are the same height but are in different groups of people that were used to train the model. This can be confusing and lead to inaccurate predictions.

To reduce variance in machine learning models, it is important to use a large and representative sample of data to train the model, and to carefully select and tune the model’s parameters. This can help the model make more stable and reliable predictions.

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