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 981–990 of 1706 papers

TitleStatusHype
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods—0
Character Feature Engineering for Japanese Word Segmentation—0
Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
Plasmodium Detection Using Simple CNN and Clustered GLCM Features—0
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning—0
Machine Learning Pipelines with Modern Big Data Tools for High Energy PhysicsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified