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 661–670 of 1706 papers

TitleStatusHype
Generative Pre-Training from MoleculesCode1
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package—0
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the OvenCode0
Sequence-to-Sequence Learning with Latent Neural GrammarsCode1
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Personality Trait Identification Using the Russian Feature Extraction Toolkit—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified