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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1360 of 1706 papers

TitleStatusHype
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks—0
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods—0
Clinical Event Detection with Hybrid Neural Architecture—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Extracting Drug-Drug Interactions with Attention CNNs—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text—0
Show:102550
← PrevPage 136 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified