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Amazon Machine Learning
Developer Guide (Version Latest)

Solving Business Problems with Amazon Machine Learning

You can use Amazon Machine Learning to apply machine learning to problems for which you have existing examples of actual answers. For example, if you want to use Amazon Machine Learning to predict if an email is spam, you will need to collect email examples that are correctly labeled as spam or not spam. You then can use machine learning to generalize from these email examples to predict how likely new email is spam or not. This approach of learning from data that has been labeled with the actual answer is known as supervised machine learning.

You can use supervised ML approaches for these specific machine learning tasks: binary classification (predicting one of two possible outcomes), multiclass classification (predicting one of more than two outcomes) and regression (predicting a numeric value).

Examples of binary classification problems:

  • Will the customer buy this product or not buy this product?

  • Is this email spam or not spam?

  • Is this product a book or a farm animal?

  • Is this review written by a customer or a robot?

Examples of multiclass classification problems:

  • Is this product a book, movie, or clothing?

  • Is this movie a romantic comedy, documentary, or thriller?

  • Which category of products is most interesting to this customer?

Examples of regression classification problems:

  • What will the temperature be in Seattle tomorrow?

  • For this product, how many units will sell?

  • How many days before this customer stops using the application?

  • What price will this house sell for?