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automated-feature-engineering

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nni

microsoft / nni

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An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

python data-science machine-learning deep-learning neural-network tensorflow machine-learning-algorithms pytorch distributed feature-extraction hyperparameter-optimization feature-engineering nas bayesian-optimization automl automated-machine-learning model-compression neural-architecture-search deep-neural-network automated-feature-engineering
  • Updated Aug 22, 2021
  • Python

guan-yuan / awesome-AutoML-and-Lightweight-Models

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A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

tensorflow pytorch hyperparameter-optimization awesome-list quantization nas automl model-compression neural-architecture-search meta-learning architecture-search quantized-training model-acceleration automated-feature-engineering quantized-neural-network
  • Updated Jun 19, 2021
Home-Credit-Default-Risk-Recognition

abhishekdbihani / Home-Credit-Default-Risk-Recognition

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The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

scikit-learn feature-selection xgboost hyperparameter-optimization lightgbm hyperopt feature-engineering bayesian-optimization hyperparameter-tuning binary-classification imbalanced-data automl loan-default-prediction udacity-machine-learning-nanodegree f1-score automated-feature-engineering featuretools auc-roc-curve auc-roc-score k-fold-cross-validation
  • Updated Jul 1, 2020
  • Jupyter Notebook

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