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 1151–1160 of 1706 papers

TitleStatusHype
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches—0
TLab: Traffic Map Movie Forecasting Based on HR-NET—0
Token-Level Metaphor Detection using Neural Networks—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
Topological Data Analysis for Portfolio Management of Cryptocurrencies—0
Toward Efficient Automated Feature Engineering—0
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes—0
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified