// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
One-Hot Encoding
A technique to convert categorical data into a numerical format that machine learning models can understand, creating new binary columns for each category.
TECHNICAL DEFINITION
A categorical feature encoding scheme that transforms nominal categorical variables into a binary vector representation, where a new binary column is created for each unique category, and a '1' indicates the presence of that category.
BACKGROUND
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Because self-attention alone is permutation-invariant, transformers inject positional information, typically through positional encodings or learned positional embeddings, so token order can affect the output.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Dummy encoding
- binary encoding (for categories)
- one-of-K encoding
USAGE NOTE
One-hot encoding is commonly used to prepare categorical features for algorithms that require numerical input.
DEVELOPERS
Organizations developing technology related to One-Hot Encoding.
Maintains Scikit-learn, the de-facto standard library for classical machine learning in Python. Its `preprocessing.OneHotEncoder` is one of the most widely used tools by data scientists for transforming categorical features into a numerical format.
Develops and maintains TensorFlow, an open-source machine learning platform. Its Keras API provides multiple methods for one-hot encoding, such as the `tf.one_hot` function and `CategoryEncoding` layers, which are fundamental for preparing categorical data for neural networks.
Leads the development of PyTorch, a popular open-source deep learning framework. PyTorch includes the `torch.nn.functional.one_hot` function, a core utility for converting integer-encoded categorical variables into the one-hot format.
Maintains the pandas library, a foundational tool for data manipulation and analysis in Python. The library's `pandas.get_dummies()` function is a simple and highly popular method for performing one-hot encoding on DataFrame columns.
Oversees the Apache Spark project, a distributed computing system. Spark's MLlib library contains a `OneHotEncoder` designed for use in scalable machine learning pipelines on large datasets, enabling this crucial preprocessing step in big data environments.
A company founded by the creators of Apache Spark, Databricks provides a unified data and AI platform. The platform heavily utilizes and simplifies Spark MLlib's capabilities, including its `OneHotEncoder`, to streamline data preprocessing for enterprise-scale machine learning.
Develops the RAPIDS suite of open-source software libraries for executing end-to-end data science pipelines on GPUs. Its `cuDF` library offers a GPU-accelerated version of one-hot encoding (`get_dummies`), drastically speeding up this preprocessing step for large datasets.
Provides an open-source AI and machine learning platform that automates many aspects of the ML pipeline. The platform automatically handles categorical features, often using one-hot encoding or similar techniques internally without requiring manual implementation.