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 941–950 of 1706 papers

TitleStatusHype
Practical Lessons on Optimizing Sponsored Products in eCommerce—0
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions—0
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks—0
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
Predict Future Sales using Ensembled Random Forests—0
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model—0
Predicting Bandwidth Utilization on Network Links Using Machine Learning—0
Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data—0
Predicting Depression for Japanese Blog Text—0
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified