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 211–220 of 1706 papers

TitleStatusHype
Artificial Intelligence Technology analysis using Artificial Intelligence patent through Deep Learning model and vector space model—0
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
A Sentence Interaction Network for Modeling Dependence between Sentences—0
A Simple and Effective Approach to the Story Cloze Test—0
A Simple and Effective Dependency Parser for Telugu—0
A simple framework for contrastive learning phases of matter—0
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting—0
Approximation Ratios of Graph Neural Networks for Combinatorial Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified