SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 691–700 of 1706 papers

TitleStatusHype
Capturing ``attrition intensifying'' structural traits from didactic interaction sequences of MOOC learners—0
A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering—0
Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified