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 391–400 of 1706 papers

TitleStatusHype
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Classifying single-qubit noise using machine learning—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Arabic Named Entity Recognition: What Works and What's Next—0
Clickbait detection using word embeddings—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Clinical Event Detection with Hybrid Neural Architecture—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified