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 831–840 of 1706 papers

TitleStatusHype
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
Estimating Brain Age with Global and Local Dependencies—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
GraphCNNpred: A stock market indices prediction using a Graph based deep learning system—0
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models—0
A Numbers Game: Numeric Encoding Options with Automunge—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified