to normalize features: when features have different scales and you use distance-based methods

Why do some things need to be adjusted to compare fairly?

Imagine you're trying to compare the heights of different plants in a garden, but some plants grow much taller than others.

To make a fair comparison, you need to adjust the heights so they're on the same scale. This way, you can easily see which plants are taller or shorter.

Example

If Plant A is 10 inches tall and Plant B is 30 inches tall, you can think of Plant B as being 3 times taller than Plant A.

Remember this

Normalizing features helps you compare things on the same scale.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

Related concepts

to standardize: when you need zero mean and unit variance for gradient-based optimization

Why do we need to make data uniform before training a model?

L1 vs L2 regularization: L1 gives sparsity (feature selection), L2 gives small weights

L1 regularization: L1 = L2 + sparsity; L2 regularization: L2 = L1 + small weights

Model selection

Why do we sometimes choose simpler explanations over complex ones?

Ridge regression

Why do straight lines sometimes fail to fit our data perfectly?

Prior probability

Why do we sometimes need to start with an educated guess before diving into new data?

score matching does: learns the gradient of the log-density without normalizing

Ever wonder how we can compare apples and oranges fairly in studies?

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