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 1561–1570 of 1706 papers

TitleStatusHype
Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling—0
This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering—0
``Why Should I Trust You?'': Explaining the Predictions of Any ClassifierCode0
Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks—0
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization—0
Fast and Accurate Performance Analysis of LTE Radio Access Networks—0
Neural Recovery Machine for Chinese Dropped Pronoun—0
The hunvec framework for NN-CRF-based sequential taggingCode0
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified