Machine Learning Terminology

Labels#

• A label is the thing we’re predicting
• ex: email is spam or not spam

Features#

• A feature is an input variable. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features
• spam detector ex:
  • words in email text
  • sender’s address
  • time of email
  • email contains the phrase “one weird trick”

Examples#

• An example is a particular instance of data. We break examples into 2 categories:
• labeled examples
  • includes both feature(s) and the label
  • used to train the model
  • spam detector eex:
    • emails that the users have explicitly marked as “spam” or “not spam”
• unlabeled examples
  • contains features but not the label
  • we use the model to predict the label on unlabeled examples
  • spam detector ex:
    • new emails that humans haven’t yet labeled

Models#

• A model is an algorithm that defines that the relationship between feature and labels
• 2 phases of a model’s life
  • Training means creating or learning the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label
  • Inference means applying the trained model to unlabeled examples. That is, you use the trained model to make useful predictions

Check out this article for more on Machine Learning terms

“Framing: Key ML Terminology | Machine Learning Crash Course.”
Google, Google,developers.google.com/machine-learning/crash-course/framing/ml-terminology

Created By: WHS Comp Sci Club Officers