Back to the Index

Feature Type

What are feature types in machine learning?

‍A feature type defines the set of valid encodings (model-dependent transformations) that can be performed on a feature value. The standard feature types are categorical (ordinal or nominal), numerical (interval or ratio), and array types (lists or embeddings).

Feature Types

The above figure is a taxonomy for data types for features; you can read more about it in the article about feature types for machine learning.

Why is it important to understand feature types?

‍Feature types determine how feature values can be encoded and represented in a machine learning model. Different types of features require different types of encoding and processing, and using the wrong encoding for a feature type can lead to inaccurate or suboptimal model performance.

Interested for more?

Does this content look outdated? If you are interested in helping us maintain this, feel free to contact us.
F
AI Lakehouse
AI Pipeline
Auto-regressive Models
AutoML
F
Backfill features
Backfill training data
Backpressure for feature stores
Batch Inference Pipeline
F
CI/CD for MLOps
Compound AI Systems
Context Window for LLMs
F
DAG Processing Model
Data Compatibility
Data Contract
Data Lakehouse
Data Leakage
Data Modeling
Data Partitioning
Data Pipelines
Data Quality
Data Transformation
Data Type (for features)
Data Validation (for features)
Data Warehouse
Data-Centric ML
Dimensional Modeling and Feature Stores
Downstream
Query Engines
F
ELT
ETL
Embedding
Encoding (for Features)
Entity
F
Feature
Feature Data
Feature Engineering
Feature Freshness
Feature Function
Feature Groups
Feature Logic
Feature Monitoring
Feature Pipeline
Feature Platform
Feature Reuse
Feature Selection
Feature Service
Feature Store
Feature Value
Feature Vector
Feature View
Filtering
Fine-Tuning LLMs
Flash Attention
F
Generative AI
Gradient Accumulation
Grouped Query Attention
F
Hallucinations in LLMs
Hyperparameter
Hyperparameter Tuning
F
Idempotent Machine Learning Pipelines
In Context Learning (ICL)
Inference Data
Inference Logs
Inference Pipeline
Instruction Datasets for Fine-Tuning LLMs
F
LLM Code Interpreter
LLM Temperature
LLMOps
LLMs - Large Language Models
Lagged features
LangChain
Latent Space
F
ML
ML Artifacts (ML Assets)
MLOps
MLOps Platform
MVPS
Machine Learning Infrastructure
Machine Learning Logs
Machine Learning Observability
Machine Learning Pipeline
Machine Learning Systems
Model Architecture
Model Bias
Model Deployment
Model Development
Model Evaluation (Model Validation)
Model Governance
Model Inference
Model Interpretability
Model Monitoring
Model Performance
F
Natural Language Processing (NLP)
F
Offline Store
On-Demand Features
On-Demand Transformation
Online Inference Pipeline
Online Store
Online-Offline Feature Skew
Online-Offline Feature Store Consistency
Orchestration
F
KServe
PagedAttention
Pandas UDF
Parameter-Efficient Fine-Tuning (PEFT) of LLMs
Point-in-Time Correct Joins
Precomputed Features
Prompt Engineering
Prompt Store
Prompt Tuning
Python UDF
F
RLHF - Reinforcement Learning from Human Feedback
Real-Time Machine Learning
Recommender System
Representation Learning
Retrieval Augmented Generation (RAG) for LLMs
RoPE Scaling
F
SQL UDF in Python
Sample Packing
Schema
Similarity Search
Skew
Splitting Training Data
Streaming Feature Pipeline
Streaming Inference Pipeline
F
Test Set
Theory-of-Mind Tasks
Time travel (for features)
Train (Training) Set
Training Data
Training Pipeline
Training-Inference Skew
Transformation
Two-Tower Embedding Model
Types of Machine Learning
F
Upstream
F
Validation Set
Vector Database
Versioning (of ML Artifacts)
vLLM