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 1421–1430 of 1706 papers

TitleStatusHype
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Fast and Accurate Neural Word Segmentation for ChineseCode0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Interpretation of Semantic Tweet RepresentationsCode0
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
Sentiment Analysis of Citations Using Word2vecCode0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models—0
Show:102550
← PrevPage 143 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified