Feature Engineering

311 papers with code • 1 benchmarks • 6 datasets

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.

Libraries

Use these libraries to find Feature Engineering models and implementations

Subtasks


Most implemented papers

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

qianguih/voxelnet CVPR 2018

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

Wide & Deep Learning for Recommender Systems

microsoft/recommenders 24 Jun 2016

Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

guillaumegenthial/sequence_tagging ACL 2016

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

xue-pai/FuxiCTR 13 Mar 2017

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.

Named Entity Recognition with Bidirectional LSTM-CNNs

zalandoresearch/flair TACL 2016

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.

Deep & Cross Network for Ad Click Predictions

shenweichen/DeepCTR 17 Aug 2017

Feature engineering has been the key to the success of many prediction models.

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

Atomu2014/product-nets-distributed 1 Jul 2018

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

shenweichen/DeepCTR 12 Apr 2018

In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.

Multimodal Speech Emotion Recognition and Ambiguity Resolution

Demfier/multimodal-speech-emotion-recognition 12 Apr 2019

In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

hwwang55/KGNN-LS 11 May 2019

Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.