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
Latest papers
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
Context-Based Tweet Engagement Prediction
In 2020, the RecSys Challenge invited participating teams to create models that would predict engagement likelihoods for given user-tweet combinations.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation Scoring
ai-zahran/E2E-R
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IEEE Access 2023
In the first step, the pre-trained SSL model is fine-tuned on a phoneme recognition task to obtain better representations for the pronounced phonemes.
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems
Our attack is versatile and can work in both white-box and black-box scenarios.
Native Language Identification with Big Bird Embeddings
sergeykramp/mthesis-bigbird-embeddings
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Native Language Identification (NLI) intends to classify an author's native language based on their writing in another language.
Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks
tommydzh/diam
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Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient.
Interpolation of mountain weather forecasts by machine learning
Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts.
TrajPy: empowering feature engineering for trajectory analysis across domains
The TrajPy package was developed in Python 3 and released under the GNU GPL-3 license.
Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models
sruthisudheer/comment-classification-of-c-code
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The performance of the classical bag of words model and transformer-based models were explored to identify significant features from the given training corpus.
