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 571–580 of 1706 papers

TitleStatusHype
Computing Committor Functions for the Study of Rare Events Using Deep Learning—0
A simple framework for contrastive learning phases of matter—0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Computational Models for Academic Performance Estimation—0
A Simple and Effective Dependency Parser for Telugu—0
A Local Detection Approach for Named Entity Recognition and Mention Detection—0
A deep learning model for estimating story points—0
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes—0
Complex Word Identification: Convolutional Neural Network vs. Feature Engineering—0
A Simple and Effective Approach to the Story Cloze Test—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified