AI Engineering Degree 2025 – 400 Free Practice Questions to Pass the Exam

In a dataset, the columns typically represent features, which are the individual measurable properties or characteristics of the data being collected. Each feature column contains values that correspond to a particular aspect of the data points or observations represented in the rows of the dataset.

For instance, in a dataset concerning housing prices, columns might include features such as square footage, number of bedrooms, or location. These features are crucial for analytical tasks, as they help define the attributes that are used in modeling and predicting outcomes.

While it is important to understand that the terms 'labels,' 'observations,' and 'variables' are also related to dataset structure, they refer to different concepts. Labels specifically refer to the outcomes or target values in supervised learning, observations are the individual data entries or instances (usually represented as rows), and variables can refer to any measurable attribute in statistics but may not specifically represent the concept of features in all contexts. Thus, identifying the columns as features accurately captures their role in the dataset.