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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 731–740 of 1706 papers

TitleStatusHype
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Fast and Accurate Performance Analysis of LTE Radio Access Networks—0
Fast and Accurate Reordering with ITG Transition RNN—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified