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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 1251–1275 of 1706 papers

TitleStatusHype
PyRATA, Python Rule-based feAture sTructure AnalysisCode0
Attention for Implicit Discourse Relation Recognition—0
Reusable workflows for gender prediction—0
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering—0
DeepTriangle: A Deep Learning Approach to Loss ReservingCode0
Data-Driven Investigative Journalism For Connectas Dataset—0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
False Information on Web and Social Media: A SurveyCode0
Event Extraction with Generative Adversarial Imitation Learning—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition—0
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR PredictionCode0
A Simple and Effective Approach to the Story Cloze Test—0
PotentialNet for Molecular Property Prediction—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
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
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
URLNet: Learning a URL Representation with Deep Learning for Malicious URL DetectionCode0
Online Compact Convexified Factorization Machine—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified