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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 1011–1020 of 1706 papers

TitleStatusHype
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
Robust PDF Document Conversion Using Recurrent Neural Networks—0
Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings—0
Robust Tracking Using Region Proposal Networks—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
Role of Morpho-Syntactic Features in Estonian Proficiency Classification—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training—0
Rule-based vs. Neural Net Approaches to Semantic Textual Similarity—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified