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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 871–880 of 1706 papers

TitleStatusHype
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks—0
Better Model Selection with a new Definition of Feature Importance—0
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic ViewCode0
Ensemble learning of diffractive optical networks—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach—0
Patient Cohort Retrieval using Transformer Language Models—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
Topological Data Analysis for Portfolio Management of Cryptocurrencies—0
Computational Models for Academic Performance Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified