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 1101–1110 of 1706 papers

TitleStatusHype
User Intent Prediction in Information-seeking ConversationsCode0
Recurrent Neural Networks for Time Series Forecasting—0
A Neural Network Based Explainable Recommender System—0
Weakly-Supervised Hierarchical Text ClassificationCode0
Feedforward Neural Network for Time Series Anomaly Detection—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting—0
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction—0
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity MeasureCode0
Time Series Featurization via Topological Data Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified