grid-search
There are 229 repositories under grid-search topic.
LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
databricks/spark-sklearn
(Deprecated) Scikit-learn integration package for Apache Spark
dezoito/ollama-grid-search
A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
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
jia-zhuang/xgboost-lightgbm-hyperparameter-tuning
Bayesian Optimization and Grid Search for xgboost/lightgbm
JuliaAI/MLJTuning.jl
Hyperparameter optimization algorithms for use in the MLJ machine learning framework
WLOGSolutions/telco-customer-churn-in-r-and-h2o
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
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
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
neuro-ml/reskit
A library for creating and curating reproducible pipelines for scientific and industrial machine learning
cstsunfu/dlk
A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo
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
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.
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.
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
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
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.
QUEST-UWMadison/OSCAR
OSCAR: configure and debug variational quantum algorithms efficiently
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.
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.
sushant1827/Human-Activity-Recognition-with-Smartphones
Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.
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
abg3/Disaster-Response-ML-Project
This project involves ETL and a Machine Learning Pipeline implementation that classifies tweets to assist people with a disaster emergency response using a Flask Web App.