Data & representations · Glossary term
What is Feature?
An individual measurable property of the data. In classical ML, you engineer features by hand. In deep learning, the network learns features automatically from raw data.
“A column in a dataset.”
What is the common confusion about Feature?
A stored column can contain several useful features, and a learned representation can contain features with no simple human label.
Learn Feature in the course
Lessons that name Feature in a title or section
- Feature Engineering & Selection
A good feature is worth a thousand data points. Implement numerical transforms (standardization, min-max scaling, log transform, binning) and explain when each is appropriate.
- Feature Selection
More features is not better. The right features is better. Language: Python Implement filter methods (variance threshold, mutual information, chi-squared) and wrapper methods (RFE, forward…
- Spectrograms, Mel Scale & Audio Features
Neural nets do not consume raw waveforms well. They consume spectrograms. They consume mel spectrograms even better. Every ASR, TTS, and audio classifier in 2026 lives or dies by this single…
- A/B Testing LLM Features — GrowthBook, Statsig, and the Vibes Problem
Traditional A/B testing was not built for non-deterministic LLMs. The critical distinction: evals answer "can the model do the job?" A/B tests answer "do users care?" Both are required; shipping on…
- Decision Trees and Random Forests
A decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML. Language: Python Implement Gini impurity, entropy, and information gain calculations to find…
- K-Nearest Neighbors and Distances
Store everything. Predict by looking at your neighbors. The simplest algorithm that actually works. Language: Python Implement KNN classification and regression from scratch with configurable K and…
- Naive Bayes
The "naive" assumption is wrong, and it works anyway. That's the beauty of it. Language: Python Implement Multinomial Naive Bayes from scratch with Laplace smoothing for text classification.
- Time Series Fundamentals
Past performance does predict future results -- if you check for stationarity first. Language: Python Decompose a time series into trend, seasonality, and residual components and test for…
Covered in Phase 02: ML Fundamentals, Phase 04: Computer Vision, Phase 05: NLP: Foundations to Advanced, Phase 06: Speech & Audio, Phase 10: LLMs from Scratch, Phase 12: Multimodal AI, Phase 13: Tools & Protocols, Phase 14: Agent Engineering and Phase 17: Infrastructure & Production.
Related terms
- EmbeddingA learned mapping from discrete items (words, images, users) to dense vectors in continuous space, where similar items end up close together
- Latent SpaceA learned representation space whose coordinates encode factors useful to a model. It may be lower-dimensional than the input, but…
- Inductive BiasStructural or statistical assumptions that favor some functions or representations over others.
- CNN (Convolutional Neural Network)A neural network that uses convolution operations (sliding filters over the input) to detect local patterns.
- EigenvalueA scalar that describes how a linear transformation scales a corresponding nonzero eigenvector without changing its direction.
- Transfer LearningStarting from representations or parameters learned on one data distribution or objective and adapting them for another.
More terms in Data & representations
This entry comes from glossary/terms.md on GitHub. Browse all 250 glossary terms.