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

A handy tool for common machine learning models' hyper-parameter tuning.

Related tags

Overview

Common machine learning models' hyperparameter tuning

Owner

Kevin Hu

A concept I came up which ditches the idea of "layers" in a neural network.

Anytime Learning At Macroscale

Evidently helps analyze machine learning models during validation or production monitoring

Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.

Bodywork deploys machine learning projects developed in Python, to Kubernetes.

💀mummify: a version control tool for machine learning

Open MLOps - A Production-focused Open-Source Machine Learning Framework

A Collection of Conference & School Notes in Machine Learning 🦄📝🎉

Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Automated Time Series Forecasting

Lseng-iseng eksplor Machine Learning dengan menggunakan library Scikit-Learn

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Azure MLOps (v2) solution accelerators.

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning

This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform.

Predico Disease Prediction system based on symptoms provided by patient- using Python-Django & Machine Learning

Flightfare-Prediction - It is a Flightfare Prediction Web Application Using Machine learning,Python and flask

Python package for causal inference using Bayesian structural time-series models.

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.