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 451–460 of 1706 papers

TitleStatusHype
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Cross-lingual Short-text Matching with Deep Learning—0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
Democratizing AI: Non-expert design of prediction tasks—0
CTSys at SemEval-2018 Task 3: Irony in Tweets—0
Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network—0
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models—0
Customers Churn Prediction in Financial Institution Using Artificial Neural Network—0
Customer Support Ticket Escalation Prediction using Feature Engineering—0
A Comparative Analysis of Android Malware—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified