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 1551–1560 of 1706 papers

TitleStatusHype
Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks—0
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification—0
Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models—0
This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering—0
Token-Level Metaphor Detection using Neural Networks—0
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification—0
``Why Should I Trust You?'': Explaining the Predictions of Any ClassifierCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified