Features and Labels in Machine Learning
Features are the input characteristics that an AI model uses to make predictions. For example, when predicting house prices, features might include: number of bedrooms, location, floor area, and age of the house.
About This Resource
Understand the two key ingredients of supervised learning: features (the inputs AI looks at) and labels (the answers AI learns to predict).
ML fundamentals resource suitable for Class 7–9 students.
What Students Will Learn
- Define features and labels with real-world examples
- Identify features and labels in a given dataset
- Explain how features affect prediction quality
- Understand the concept of feature selection
Resources Available
Questions & Answers
What are features in machine learning?
Features are the input characteristics that an AI model uses to make predictions. For example, when predicting house prices, features might include: number of bedrooms, location, floor area, and age of the house.
What are labels in machine learning?
A label is the answer or output that the model is trying to predict. In our house example, the label is the actual selling price. The model learns to predict labels from features.
Why do features matter in AI?
The quality and choice of features directly affects how accurate an AI model is. If you include the wrong features — or miss important ones — the model will make poor predictions.
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