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 521–530 of 1706 papers

TitleStatusHype
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
An Error Analysis Tool for Natural Language Processing and Applied Machine Learning—0
Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information—0
Adversarial training for tabular data with attack propagation—0
Detecting Troll Tweets in a Bilingual Corpus—0
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
Automatic Features for Essay Scoring -- An Empirical Study—0
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified