Feature

How it is used

Common in classic machine learning on tables. Engineers select, create, scale and drop features; 'feature engineering' means designing useful features by hand, and 'feature importance' shows which ones affect predictions most.

Example sentences

1. Adding the day of the week as a feature improved the demand forecast.

2. These two features are almost identical, so we can drop one of them.

Common collocations

• feature engineering
• feature importance
• input features
• feature selection

Related terms

• labelThe correct answer attached to a training example, such as 'spam' for an email or 'cat' for a photo, which a supervised model learns to predict.
• datasetA collection of examples, such as images, texts or table rows, gathered and organized for training, testing or analyzing a model.
• preprocessingThe steps that clean and prepare raw data before a model uses it, such as removing duplicates, filling in missing values, resizing images or normalizing text.
• embeddingA list of numbers (a vector) that represents a word, sentence, image or other item in such a way that items with similar meanings end up close to each other.

This word in other fields

The same English word can have a different meaning in another field:

• feature · Software Engineering
• feature · Data Science & Analytics
• feature · Product Management
• feature · Journalism & Media

More terms: Data and Features

• training data
• annotation
• ground truth
• data augmentation
• class imbalance
• data leakage