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 611–620 of 1706 papers

TitleStatusHype
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods—0
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition—0
EEG Based Emotion Sensing using convolutional neural networks—0
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
Effective Representations of Clinical Notes—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Empty Category Detection With Joint Context-Label Embeddings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified