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 91–100 of 1706 papers

TitleStatusHype
Simplified DOM Trees for Transferable Attribute Extraction from the WebCode1
Statistical learning for accurate and interpretable battery lifetime predictionCode1
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global InferenceCode1
Enabling Collaborative Data Science Development with the Ballet FrameworkCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Yelp Review Rating Prediction: Machine Learning and Deep Learning ModelsCode1
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code MatchingCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small DatasetsCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified