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 1276–1300 of 1706 papers

TitleStatusHype
Extractive Text Summarization using Neural Networks—0
Interpreting Complex Regression Models—0
Global Pose Estimation with an Attention-based Recurrent Network—0
Event Nugget Detection with Forward-Backward Recurrent Neural Networks—0
Democratizing AI: Non-expert design of prediction tasks—0
client2vec: Towards Systematic Baselines for Banking Applications—0
URLNet: Learning a URL Representation with Deep Learning for Malicious URL DetectionCode0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
Online Compact Convexified Factorization Machine—0
Heuristic Feature Selection for Clickbait Detection—0
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task LearningCode0
Evaluating approaches for supervised semantic labelingCode0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Neural Feature Learning From Relational Database—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching ModelCode0
Learning Feature Representations for Keyphrase Extraction—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
Learning Representations from Road Network for End-to-End Urban Growth Simulation—0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical PropertiesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified