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 1581–1590 of 1706 papers

TitleStatusHype
Cross-lingual Short-text Matching with Deep Learning—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
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective—0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified