Collecting Labeled Data - Amazon Machine Learning

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Collecting Labeled Data

ML problems start with data—preferably, lots of data (examples or observations) for which you already know the target answer. Data for which you already know the target answer is called labeled data. In supervised ML, the algorithm teaches itself to learn from the labeled examples that we provide.

Each example/observation in your data must contain two elements:

  • The target – The answer that you want to predict. You provide data that is labeled with the target (correct answer) to the ML algorithm to learn from. Then, you will use the trained ML model to predict this answer on data for which you do not know the target answer.

  • Variables/features – These are attributes of the example that can be used to identify patterns to predict the target answer.

For example, for the email classification problem, the target is a label that indicates whether an email is spam or not spam. Examples of variables are the sender of the email, the text in the body of the email, the text in the subject line, the time the email was sent, and the existence of previous correspondence between the sender and receiver.

Often, data is not readily available in a labeled form. Collecting and preparing the variables and the target are often the most important steps in solving an ML problem. The example data should be representative of the data that you will have when you are using the model to make a prediction. For example, if you want to predict whether an email is spam or not, you must collect both positive (spam emails) and negative (non-spam emails) for the machine learning algorithm to be able to find patterns that will distinguish between the two types of email.

Once you have the labelled data, you might need to convert it to a format that is acceptable to your algorithm or software. For example, to use Amazon ML you need to convert the data to comma-separated (CSV) format with each example making up one row of the CSV file, each column containing one input variable, and one column containing the target answer.