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 421–430 of 1706 papers

TitleStatusHype
Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models—0
A Simple and Effective Approach to the Story Cloze Test—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified