There are 2 repositories under model-comparison topic.
A Python library for amortized Bayesian workflows using generative neural networks.
Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
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
This is the repo for a python package that does model comparison between different regression models.
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.
NeurIPS 2018. Linear-time model comparison tests.
Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python
Matlab command-line functions for supporting Simulink model comparison
"Interactive Polar Diagrams for Model Comparison" by Aleksandar Anžel, Dominik Heider, and Georges Hattab
Using models to understand relationships and make predictions.
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)
This repository contains my online payment fraud detection project using Python
MADYS: isochronal parameter determination for young stellar and substellar objects
Classification model on Titanic: Tragic shipwreck with EDA. Secured Accuracy Score of ~0.78.
Awesome Collaborated Project of Master's Program Analysis with Ranjith Kumar Govindarajan.
Regression model on Taxi Fare Data with EDA. The data is taken from a Hackathon ( Data Science Student Championship 2023 ) on MachineHack.
Repositorio para el curso intersemestral "Temas Selectos en Estadística" para la Facultad de Psicología, UNAM.
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.
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.
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.
Regression problem predicting Boston house prices in RStudio
Over the past months, we have seen a significant racial justice reckoning happening across the country since the killing of George Floyd by a police officer in May 2020. This incident sparked a redirection of attention to similar lives that had been lost at the hands of officers, leading to calls for re-evaluation of the role and power that police hold. In order for stakeholders like activism groups and local policymakers to make the most change in the quickest and most effective manner in response to these calls, the data code and report strived to answer a questions that will enable this. The primary tool used was R, with ggplot and machine learning packages.
Explaining microbial scaling laws using Bayesian inference
Quantization Aware Training