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Glossary

  • Instance: The thing about which you want to make a prediction. For example, the instance might be a web page that you want to classify as either "about cats" or "not about cats".
  • Label: An answer for a prediction task ­­ either the answer produced by a machine learning system, or the right answer supplied in training data. For example, the label for a web page might be "about cats".
  • Feature: A property of an instance used in a prediction task. For example, a web page might have a feature "contains the word 'cat'".
  • Feature Column: A set of related features, such as the set of all possible countries in which users might live. An example may have one or more features present in a feature column. "Feature column" is Google-specific terminology. A feature column is referred to as a "namespace" in the VW system (at Yahoo/Microsoft), or a field.
  • Example: An instance (with its features) and a label.
  • Model: A statistical representation of a prediction task. You train a model on examples then use the model to make predictions.
  • Metric: A number that you care about. May or may not be directly optimized.
  • Objective: A metric that your algorithm is trying to optimize.
  • Pipeline: The infrastructure surrounding a machine learning algorithm. Includes gathering the data from the front end, putting it into training data files, training one or more models, and exporting the models to production.
  • Click-through Rate The percentage of visitors to a web page who click a link in an ad.

Sources

https://developers.google.com/machine-learning/guides/rules-of-ml