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

For data preparation, 'feature normalization' involves adjusting the data to have a mean of zero and a standard deviation of one. An analyst might say, 'By performing feature normalization, we can speed up the convergence of neural networks and improve the accuracy of our predictions.'

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.

Business

Scenario:

The sales data from different regions has varied scales. We need to figure out a way to analyse them on the same basis.

Response:

Let's conduct feature-normalization to make the sales data from different regions more comparable.