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 1511–1520 of 1706 papers

TitleStatusHype
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Capturing ``attrition intensifying'' structural traits from didactic interaction sequences of MOOC learners—0
Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media—0
Character Feature Engineering for Japanese Word Segmentation—0
Character-level Supervision for Low-resource POS Tagging—0
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified