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 1691–1700 of 1706 papers

TitleStatusHype
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Effective Representations of Clinical Notes—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Effort Estimation in Named Entity Tagging Tasks—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified