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 511–520 of 1706 papers

TitleStatusHype
Object-Category Aware Reinforcement Learning—0
Less is More: Facial Landmarks can Recognize a Spontaneous SmileCode0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Point Cloud Recognition with Position-to-Structure Attention Transformers—0
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Estimating Brain Age with Global and Local Dependencies—0
Self-Optimizing Feature Transformation—0
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified