Common machine learning models' hyperparameter tuning

This repo is for a collection of hyper-parameter tuning for "common" machine learning models, including:

• Linear SVM (Grid Search),
• RBF-Kernel SVM (Grid Search),
• Radom Forest (Bayesian Optimization),
• XG Boost(Bayesian Optimization),
• Logistic Regression (Grid Search),
• k-Nearest Neighbors (Grid Search),
• Extra Trees (Bayesian Optimization).

All hyper-parameters' searching space are set by empirical knowledge. You may play with it own your own.

If you find this tool is usefull, we will be glad if you can cite us in your paper :-)

AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context (https://link.springer.com/article/10.1007/s42486-021-00073-3)

Recommended Packages:

• Python 3.6+
• Numpy 1.19.5
• scikit-learn 1.0.1
• xgboost 1.5.1

If you are using an Intel chip, you may need this to accelerate the computing:

• scikit-learn-intelex 2021.2.2

If you want to use the Bayesian Optimization, you need install this package:

• hyperopt 0.2.7