grid-search

databricks/spark-sklearn

(Deprecated) Scikit-learn integration package for Apache Spark

williamFalcon/test-tube

Python library to easily log experiments and parallelize hyperparameter search for neural networks

gmontamat/gentun

Hyperparameter tuning for machine learning models using a distributed genetic algorithm

JuliaAI/MLJTuning.jl

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

jia-zhuang/xgboost-lightgbm-hyperparameter-tuning

Bayesian Optimization and Grid Search for xgboost/lightgbm

WLOGSolutions/telco-customer-churn-in-r-and-h2o

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

romainkp/stremr

Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data

enricoschumann/NMOF

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). This repository mirrors https://gitlab.com/NMOF/NMOF .

buomsoo-kim/Machine-learning-toolkits-with-python

Machine learning toolkits with Python

farhanchoudhary/Machine_Learning_A-Z_All_Codes_and_Templates

All codes, both created and optimized for best results from the SuperDataScience Course

eHarmony/spotz

Spark Parameter Optimization and Tuning

Architectshwet/Loan-prediction-using-Machine-Learning-and-Python

To design a predictive model using xgboost and voting ensembling techniques and extract insights from the data using pandas, seaborn and matplotlib

cstsunfu/dlk

A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo

neuro-ml/reskit

A library for creating and curating reproducible pipelines for scientific and industrial machine learning

Davisy/Hyperparameter-Optimization-Techniques

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

sourcecode369/ml-algorithms-on-scikit-and-keras

Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..

hyeonsangjeon/Hyperparameters-Optimization

Hyperparameters-Optimization

KunBetter/GridSearch

real-time grid search engine

davidenunes/exp

python experiment management toolset

franneck94/TensorCross

Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets

yh1008/deepLearning

MNIST, SVHN, Transfered Learning, DNN, CNN

bmcmenamin/thresholdOut-explorations

playing with Dwork's adaptive holdout and how to use it for a grid-search

sandipanpaul21/Tree-Based-Models-in-Python

Tree based algorithm in machine learning including both theory and codes. Topics including from decision tree regression and classification to random forest tree and classification. Grid Search is also included.

DavidCico/Univariate-time-series-analysis-of-cryptocurrency-data-with-ARIMA-and-SARIMA-and-hypergrid-search

Two Jupyter Notebooks written in Python, treating of time series analysis with ARIMA and its seasonal counterpart.

devssh/svm

Learn to use Support Vector Machines in Python(sklearn) and R

mainkoon81/Study-09-MachineLearning-A

With some projects to develop "TOOLs" for better Modeling

dumebi/Predicting-Boston-Housing-Prices

Udacity Machine Learning Course Predicting Boston Housing Prices

SadmanSakib93/ANN-Stratified-K-Fold-Cross-Validation-Keras-Tensorflow

This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.

asaficontact/project_floodlight

Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.

bhavinidata/AccidentsSeverityPredictionML

Web app to Predict of US traffic accidents severity based on weather conditions like visibility, temperature and weather categories like rain, snow, foggy etc as well as based on US location and the day of the week.

Paritoshyadav/Minimizing-Churn-Rate-Through-Analysis-of-Financial-Habits

Build a Machine learning model to direct customers to subscribe product through a app behavior analysis

yukai-yang/zoomgrid

an R package implementing the grid search optimization algorithm with a zoom

Andrew-Carl/Pump-it-Up-Data-Mining-the-Tanzania-Water-Table

Predicting functionality of groundwater pumps throughout Tanzania.

QUEST-UWMadison/OSCAR

OSCAR: configure and debug variational quantum algorithms efficiently

KhaledTofailieh/Reactions-Regression