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 331–340 of 1706 papers

TitleStatusHype
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in textCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Binary Classification as a Phase Separation ProcessCode0
Binary Classification in Unstructured Space With Hypergraph Case-Based ReasoningCode0
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy TranscriptsCode0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the OvenCode0
Boosting Relational Deep Learning with Pretrained Tabular ModelsCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified