Standard 2 stops to get here · leads to 2
Feature Engineering
The process of selecting, transforming, and creating input features to improve model performance.
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2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Feature Engineering · you are here ✓ understood
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Foundations Feature Selection Choosing the most relevant features from available data to reduce dimensionality and improve model performance. Foundations Data Preprocessing Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding). Foundations One-Hot Encoding Converting categorical variables into binary vectors with one element set to 1 and others to 0. Shipping AI Feature Store A centralized platform for managing, storing, and serving features for ML models. Foundations Feature Importance Measures indicating which features contribute most to model predictions, useful for interpretation and selection.