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Also known as: Vector conversion
Converting raw data into numeric vectors suitable for efficient computation and machine learning.
DESCRIPTION
Vectorization maps inputs like text, images, or categorical fields to numeric vectors that models can process. Classical techniques include one-hot encoding, bag-of-words, and TF‑IDF; modern approaches rely on learned embeddings. In numerical computing, vectorization also refers to batch operations that exploit optimized linear algebra. Effective vectorization balances informativeness, dimensionality, and computational cost. It is foundational for training and serving ML systems.
ALTERNATIVE DEFINITIONS
Wikipedia
In pattern recognition, a feature vector is an n-dimensional vector of numerical features that represent some object.
Grounds vector representation as numerical descriptors of objects.
scikit-learn
Feature extraction converts raw data into numerical features that can be processed by machine learning algorithms.
Explicitly ties extraction to numerical ML processing.
TensorFlow
TextVectorization maps strings to integer sequences or dense representations as part of an input processing pipeline.
Operational definition for text data in deep learning pipelines.
RELATED TERMS
Embeddings Feature Extraction Bag-of-Words Term Frequency–Inverse Document Frequency One-Hot Encoding Word2Vec Text Embedding Feature Reduction Sparse Representation NumPy Vectorization
COMPETING TERMS
Symbolic Representation
RELATIONS
- Narrower: Broadcasting
KNOWLEDGE AREAS
AI Data Science Data Engineering Programming Languages
TARGET AUDIENCE
Data Scientist AI Engineer Data Engineer Analytics Engineer Software Developer
EXTERNAL SOURCES
- Wikipedia - Feature vector
- scikit-learn - Guide to feature extraction
- TensorFlow - Keras text vectorization layer
- PyTorch - Embedding layer documentation
TOP QUESTIONS
Why is Vectorization essential for machine learning?
Models operate on numbers; vectorization translates raw inputs into numerical features enabling training and inference.
Category: Relevancy
What are common text Vectorization methods?
Bag‑of‑words, TF‑IDF, hashing trick, and learned embeddings like Word2Vec or transformer token embeddings.
Category: Methods
How does Vectorization differ from embeddings?
Embeddings are a learned vectorization producing dense vectors; vectorization is the broader process of numeric representation.
Category: Misunderstanding
What is numeric Vectorization in scientific computing?
It means using vectorized operations (e.g., NumPy) for parallel, optimized computation instead of Python loops.
Category: Concepts
What risks exist in Vectorization for text?
High-dimensional sparsity, out‑of‑vocabulary issues, and tokenization bias can degrade performance.
Category: Risks
How to choose between sparse and dense Vectorization?
Use sparse for linear models on high-dimensional text; use dense embeddings for deep models and semantic tasks.
Category: Guidance
Does Vectorization apply to images and audio?
Yes; images are vectorized via pixels or CNN features, audio via spectrograms or learned embeddings.
Category: Scope
How to evaluate Vectorization quality?
Assess downstream performance, cluster coherence, nearest neighbor retrieval, and calibration.
Category: Metrics
What is a common mistake in Vectorization pipelines?
Leaking preprocessing fit across train/test splits; always fit vectorizers on training only.
Category: Mistakes