Feature Engineering
393 papers with code • 1 benchmarks • 5 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.
Benchmarks
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Libraries
Use these libraries to find Feature Engineering models and implementations
EMBER
Most implemented papers
Discovering Neural Wirings
In this work we propose a method for discovering neural wirings.
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
We introduce a deep neural network for automated sarcasm detection.
Deep Voice: Real-time Neural Text-to-Speech
NVIDIA/nv-wavenet
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ICML 2017
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model.
Recurrent Attention Network on Memory for Aspect Sentiment Analysis
songyouwei/ABSA-PyTorch
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EMNLP 2017
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
This approach allows the model to capture several types of semantic information, which was not possible by the existing models.
False Information on Web and Social Media: A Survey
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact.
DeepTriangle: A Deep Learning Approach to Loss Reserving
We propose a novel approach for loss reserving based on deep neural networks.
