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 901–925 of 1706 papers

TitleStatusHype
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
DriveML: An R Package for Driverless Machine LearningCode1
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
A Kernel Two-sample Test for Dynamical Systems—0
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTCode1
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study—0
Template-based Question Answering using Recursive Neural NetworksCode0
Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Scalable Deployment of AI Time-series Models for IoT—0
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
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
DROCC: Deep Robust One-Class Classification—0
IoT Device Identification Using Deep Learning—0
Trees and Forests in Nuclear Physics—0
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified