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 911–920 of 1706 papers

TitleStatusHype
TSFEL: Time Series Feature Extraction LibraryCode2
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks—0
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature ExtractionCode0
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?Code0
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT—0
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks—0
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
A Review of Computational Approaches for Evaluation of Rehabilitation Exercises—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified