Reference 2 stops to get here
One-Hot Encoding
Converting categorical variables into binary vectors with one element set to 1 and others to 0.
Your route here
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.
- One-Hot Encoding · you are here ✓ understood
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Foundations Feature Engineering The process of selecting, transforming, and creating input features to improve model performance. Language & LLMs Embedding A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together. Foundations Data Preprocessing Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding). Foundations Normalization Scaling features to a standard range (typically 0-1 using min-max scaling) to improve model training and convergence. Often used interchangeably with standardization (mean=0, std=1), though technically distinct.