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 1141–1150 of 1706 papers

TitleStatusHype
The Randomized Causation Coefficient—0
The Recent Advances in Automatic Term Extraction: A survey—0
The Spatially-Conscious Machine Learning Model—0
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches—0
This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering—0
Three-Class Text Sentiment Analysis Based on LSTM—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Time Series Featurization via Topological Data Analysis—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified