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 691–700 of 1706 papers

TitleStatusHype
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
BERTMap: A BERT-based Ontology Alignment System—0
Predicting Bandwidth Utilization on Network Links Using Machine Learning—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
User-click Modelling for Predicting Purchase Intent—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified