CAAFE lets you semi-automate your feature engineering process based on your explanations on the dataset and with the help of language models. It is based on the paper LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering" by Hollmann, Müller, and Hutter (2023) .

May 5, 2023 · Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features …

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We present CAAFE, an approach that leverages large language models to incorporate domain knowledge into the feature engineering process, offering a promising direction for automating data science tasks while maintaining interpretability and performance.

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Dec 10, 2023 · Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features …

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Jun 18, 2023 · CAAFE is a tool that empowers data scientists to focus on more complex tasks. By generating semantically meaningful features, CAAFE not only speeds up the process but also...

We introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features for tabular datasets based on the description of the dataset.

May 19, 2023 · Context-Aware Automated Feature Engineering (CAAFE) is an automated machine learning tool that uses large language models for feature engineering in tabular datasets. It generates Python code for new features along with explanations for …

Oct 14, 2023 · CAAFE, the Comprehensive Automated Approach for Feature Engineering, exemplifies this trend by automating a crucial and resource-intensive aspect of the data preprocessing pipeline. By leveraging the power of CAAFE, data scientists can focus more on refining their models and gaining deeper insights from data, ultimately driving innovation and ...

We introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tab-ular datasets that utilizes an LLM to iteratively generate additional semantically meaningful fea-tures for tabular datasets based on the description of the dataset.

description="Context-Aware Automated Feature Engineering (CAAFE) is an automated machine learning tool that uses large language models for feature engineering in tabular datasets. It generates Python code for new features along with explanations for their utility, enhancing interpretability.",

May 5, 2023 · CAAFE paves the way for more extensive (semi-)automation in data science tasks and emphasizes the significance of context-aware solutions that can extend the scope of AutoML systems. For reproducability, we release our code and a simple demo.

Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features for tabular datasets based on the description of the dataset.

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