feature vector
A feature vector is an ordered list of numerical values that represent the characteristics (or features) of an object or phenomenon. Each value in the vector corresponds to a specific feature. For example, in a dataset describing houses, a feature vector might include values for the number of bedrooms, square footage, and age of the house. These vectors serve as the input for machine learning models, allowing them to learn from and make predictions about data. This representation is essential for the algorithms to perform tasks such as classification, regression, and clustering.
Consider a dataset of houses, where each house is described by features such as the size of the house in square feet and the number of bedrooms. A feature vector for a specific house might include these features in a numerical format, like [2000, 3], representing a 2000 square foot (ca. 186 m²) house with 3 bedrooms[4]. This vector is then used as input for machine learning models to predict outcomes, such as the house’s price.
Key Points about Feature Vectors
The quality and selection of features in a feature vector directly impact the performance of machine learning models, making feature engineering—a process of selecting, transforming, and constructing features—a critical step in the development of machine learning applications[6].
- Numerical Representation: Since machine learning models primarily work with numerical data, feature vectors convert the characteristics of observed phenomena into a format that these models can understand and work with[1].
- Ordered List: A feature vector is essentially an ordered list of numerical properties that represent input features to a machine learning model for making predictions[1].
- Dimensionality: The “n-dimensional” aspect refers to the number of features included in the vector. Each dimension corresponds to a specific feature of the object being observed[3].
- Applications Across Domains: Feature vectors find applications in various domains such as data mining, natural language processing, and computer vision. They can represent a wide range of data types, from the RGB values in an image to the presence of specific words in a text[1][2].
- Facilitates Analysis: By representing objects as feature vectors, it becomes easier to perform statistical analyses and comparisons, such as calculating the Euclidean distance between two objects[2].
- Constitutes Feature Space: When feature vectors for multiple objects are combined, they make up a feature space, which is the environment in which machine learning algorithms operate and make predictions[2].
Machine Learning: What is a Feature Vector?
Citations:
[1] https://www.iguazio.com/glossary/feature-vector/
[2] https://brilliant.org/wiki/feature-vector/
[3] https://deepchecks.com/glossary/feature-vector/
[4] https://www.hopsworks.ai/dictionary/feature-vector
[5] https://encord.com/glossary/feature-vector-definition/
[6] https://en.wikipedia.org/wiki/Feature_%28machine_learning%29