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 921–930 of 1706 papers

TitleStatusHype
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
A Kernel Two-sample Test for Dynamical Systems—0
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study—0
Template-based Question Answering using Recursive Neural NetworksCode0
Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Scalable Deployment of AI Time-series Models for IoT—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified