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

Kevin Hu

Last update: Jan 27, 2022

Overview

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

MIT-Machine Learning with Python–From Linear Models to Deep Learning

MIT-Machine Learning with Python–From Linear Models to Deep Learning | One of the 5 courses in MIT MicroMasters in Statistics & Data Science Welcome t

2 Aug 23, 2022

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Petastorm Contents Petastorm Installation Generating a dataset Plain Python API Tensorflow API Pytorch API Spark Dataset Converter API Analyzing petas

1.6k Dec 31, 2022

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 2, 2023

A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Currently a Beta-Version lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning mod

15 Aug 12, 2022

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production. Liminal provides a Domain Specific Language to build ML workflows on top of Apache Airflow.

121 Dec 28, 2022

Evidently helps analyze machine learning models during validation or production monitoring

Evidently helps analyze machine learning models during validation or production monitoring. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. Currently 6 reports are available.

3.1k Jan 7, 2023

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

FLAML - Fast and Lightweight AutoML

2.2k Jan 9, 2023

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

95 Dec 28, 2022

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

1 Nov 22, 2021

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

Related tags

Overview

Common machine learning models' hyperparameter tuning

You might also like...

MIT-Machine Learning with Python–From Linear Models to Deep Learning

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Evidently helps analyze machine learning models during validation or production monitoring

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Owner

Kevin Hu

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow

Python package for machine learning for healthcare using a OMOP common data model

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects