Feature Engineering
Feature engineering is the process of using domain knowledge to select, transform, and create the input variables (features) that a machine learning model is trained on, in order to make patterns in the data easier for the model to learn.
8 resources across 3 libraries
Glossary Terms(5)
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both…
Data Augmentation
Data augmentation is the practice of artificially expanding a training dataset by applying transformations to existing examples — such as rotating images or pa…
Feature Engineering
Feature engineering is the process of using domain knowledge to select, transform, and create the input variables (features) that a machine learning model is t…
Feature Store
A feature store is a centralized data system that stores, manages, and serves the engineered features used to train and run machine learning models, ensuring c…
AutoML
AutoML (Automated Machine Learning) refers to tools and techniques that automate parts of the machine learning pipeline — such as feature selection, model sele…
Cheat Sheets(1)
Interview Questions(2)
How to Design a Recommendation Engine
A recommendation engine splits work into an offline pipeline that trains models and precomputes candidate item lists from historical interaction data, and an o…
How to Design a News Feed Ranking System
A news feed ranking system fetches a large candidate set of recent posts from people and pages a user follows, scores each candidate with a machine-learned mod…