Important Terminologies that are used in Machine Learnings are

According to Google Machine Learning Education Department
The following terms will come up repeatedly in our discussion of effective machine learning:

• 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.