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 951–960 of 1706 papers

TitleStatusHype
Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models—0
A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks—0
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory—0
Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks—0
Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP—0
Predicting the Industry of Users on Social Media—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
Prediction Model For Wordle Game Results With High Robustness—0
Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning—0
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified