feature-normalization

Vocabulary Word

Definition

The term 'feature-normalization' is used when you want to make different pieces of data more alike, so they can be compared more accurately. This is done by changing the range of values they can take. It's like measuring everyone's height in the same units so you can compare them fairly.

Examples in Different Contexts

In machine learning preprocessing, 'feature normalization' refers to the process of scaling input variables to a common scale without distorting differences in the ranges of values. A data scientist might explain, 'Feature normalization is essential for algorithms that are sensitive to the scale of data, such as gradient descent, ensuring all features contribute equally to the model.'

Practice Scenarios

Product

Scenario:

To gauge the product's performance accurately, we need to consider the diverse range of metrics, how do we about that?

Response:

Perhaps we can eliminate the differences by implementing feature-normalization on our metrics.

Tech

Scenario:

If we are feeding different range of numerical features into our machine-learning model, how do we ensure proper comparison and accuracy?

Response:

We should apply feature-normalization on our numerical data to ensure accuracy in the machine-learning model.