model-comparison
There are 74 repositories under model-comparison topic.
stefanradev93/BayesFlow
A Python library for amortized Bayesian workflows using generative neural networks.
stan-dev/loo
loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)
Joshuaalbert/jaxns
Probabilistic Programming and Nested sampling in JAX
wknoben/MARRMoT
Modular Assessment of Rainfall-Runoff Models Toolbox - Matlab code for 47 conceptual hydrologic models
Sanaelotfi/Bayesian_model_comparison
Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
EmilianoGagliardiEmanueleGhelfi/CNN-compression-performance
A python script that automatise the training of a CNN, compress it through tensorflow (or ristretto) plugin, and compares the performance of the two networks
UBC-MDS/RegscorePy
This is the repo for a python package that does model comparison between different regression models.
gershonc/octopus-ml
A collection of handy ML and data visualization and validation tools. Go ahead and train, evaluate and validate your ML models and data with minimal effort.
wittawatj/kernel-mod
NeurIPS 2018. Linear-time model comparison tests.
AaronFlore/Forecasting-Bitcoin-Prices
Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python
alicelh/ModelWise
ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection(EuroVis 22)
chjackson/fic
R package for focused information criteria for model comparison
McSCert/Model-Comparison-Utility
Matlab command-line functions for supporting Simulink model comparison
AAnzel/Polar-Diagrams-for-Model-Comparison
"Interactive Polar Diagrams for Model Comparison" by Aleksandar Anžel, Dominik Heider, and Georges Hattab
seuwenfei/Online-payment-fraud-detection
This repository contains my online payment fraud detection project using Python
m-clark/R-III-Modeling
Using models to understand relationships and make predictions.
vsquicciarini/madys
MADYS: isochronal parameter determination for young stellar and substellar objects
advaitsave/Churn-Classification-Model-Selection
A comprehensive Churn Classification solution aimed at laying out the steps of a classification solution, including EDA, Stratified train test split, Training multiple classifiers, Evaluating trained classifiers, Hyperparameter tuning, Optimal probability threshold tuning, model comparison, model selection and Whiteboxing models for business sense. (Python)
guyabel/tidycat
Expand broom::tidy() output for categorical parameter estimates
m-pektas/BFAS
Brute Force Architecture Search
ashx010/Titanic_Analysis_Model
Classification model on Titanic: Tragic shipwreck with EDA. Secured Accuracy Score of ~0.78.
mjvakili/gambly
Searching for galaxy-assembly bias in the SDSS data
RohitLearner/Dream-Masters-Program-Analysis
Awesome Collaborated Project of Master's Program Analysis with Ranjith Kumar Govindarajan.
ashx010/taxi_fare_prediction
Regression model on Taxi Fare Data with EDA. The data is taken from a Hackathon ( Data Science Student Championship 2023 ) on MachineHack.
cheeann13/Heart-Attack-APP
Machine Learning Model Comparison, Logistic Regression, Streamlit Cloud
Christian-F-Badillo/Temas_Selectos_en_Estadistica
Repositorio para el curso intersemestral "Temas Selectos en Estadística" para la Facultad de Psicología, UNAM.
Enzo2806/KNN-DecisionTree
We investigated the performance of the K Nearest neighbours and the Decision Tree machine learning models. We compared them based on their classification accuracy on the UCI Hepatitis and Diabetic Retinopathy datasets.
Enzo2806/Logistic-Multiclass
We investigated the performance of the Logistic and Multiclass Regression models and compared their accuracies to KNN. We compared Logistic Regression and KNN based on the "IMdB reviews" dataset, while Multiclass Regression and KNN were compared based on the "20 news groups" dataset.
Enzo2806/MLP-CNN
We implemented a Multi-Layer Perceptron (MLP) model from scratch and compared its performance based on image classification accuracy on the "Fashion-MNIST" dataset to the performance of the Tensorflow Keras library's Convolutional Neural Network (CNN).
Genius98/House-Price-Prediction
The methods used in this thesis study consisted of Least Absolute Selection Operator (Lasso), Ridge, LightGBM, and XGBoost, Multiple linear regression, Ridge regression, LightGBM, XGBoost. With the use of a variety of regression methods it's being able to predict the sale price of the house. In addition, this model also helps identify which characteristics of housing were most strongly associated with price and could explain most of the price variation. Furthermore, I was able to improve models’ prediction accuracy by ensembling StackedRegressor, XGBoost and LightGBM.
NicolaZomer/Microbial_Scaling_Laws
Explaining microbial scaling laws using Bayesian inference
yihong1120/YOLOv8-qat
Quantization Aware Training
yusufesatt/model-map-comparison
This project includes a python script that creates graphs by reading data from CSV files of models trained with YOLO.