From the course: Machine Learning with SageMaker by Pearson

Unlock this course with a free trial

Join today to access over 25,200 courses taught by industry experts.

Feature scaling and encoding techniques

Feature scaling and encoding techniques - Amazon SageMaker Tutorial

From the course: Machine Learning with SageMaker by Pearson

Start my 1-month free trial Buy for my team

Feature scaling and encoding techniques

“

So I've mentioned in a previous lesson what a feature is. A feature is an individual measurable property or characteristic of the data that is used as input to a machine learning model. We have different types. Numerical, these are numbers. We have textual, for just strings of data. Categorical, which means it has to be one of these things. Or a derived feature. I have examples of all of these in a scenario on the next slide. These should be representative, informative, and appropriately scaled. We're going to talk about feature scaling here in just a minute to ensure that we maximize their utility when training our models. So here's a scenario. We want to predict customer churn. And we have features in our data set. Some features, for example, of age, gender, and region. These are demographic features. Subscription features, contract type, plan type, how long they've been a customer, some behavioral features, how long, or rather how many minutes per month they use this particular…

Contents