A feature vector is a numerical representation of an object or data point in a multidimensional space, where each dimension corresponds to a specific attribute or feature. Feature vectors are fundamental in machine learning, as they encode the essential characteristics of data, allowing models to learn patterns, make predictions, or perform clustering.
Key Characteristics:
- Dimensionality: Each value in the vector represents a specific feature or attribute of the data.
- Compact Representation: Encodes raw data into a form that machine learning models can process efficiently.
- Feature Engineering: Creating meaningful feature vectors is critical for improving model performance.
Applications:
- NLP: Word embeddings (e.g., Word2Vec) convert words into feature vectors based on semantic relationships.
- Computer Vision: Image feature vectors represent visual elements like edges, colors, or textures.
- Clustering and Classification: Maps data into a feature space where relationships can be analyzed.
- Recommender Systems: Encodes user preferences and item attributes for personalized recommendations.
Why It Matters:
Feature vectors enable machine learning algorithms to process data in a structured way, making it easier to identify patterns and relationships. High-quality feature vectors lead to better model accuracy and performance.
