sensitivity

LinearMouse is a free and open-source utility for macOS which aims to improve the experience and functionality of third-party mice.

Uncertainty treatment library

BioSTEAM's Premier Repository for Biorefinery Models and Results

Report various statistics stemming from a confusion matrix in a tidy fashion. 🎯

Toolbox for analysis of model's quality and model's description. For further details see

A Discord bot designed to convert sensitivities across multiple games. Based on the magic from KovaaK's Sensitivity Matcher.

Fast nonlinear FEA tailored for topology optimization

VARS-TOOL sensitivity analysis package in Python

Have trouble with your server going too fast? Try this new auto-slowmode Discord Bot!

An R package for applying global sensitivity analysis in physiologically based kinetic modeling

A tool for electromagnetic modelling of the head and sensitivity analysis.

PyTorch and Tf-Keras implementations of an epidemiology metric from Kaivanto (2008), suitable for imbalanced data

Combinatorial Selection of Biomarkers Made Easy

Solve complex real-life problems with the simplicity of Keras

Discovering if there exists a relationship🔗 between specific demographic characteristics in the population that would explain the tolerance in the population to report noise nuisance to the "311" department 👮🏻‍♂️

Early Prediction of Birth weight Based on Maternal Factors using Machine Learning.

Filter and plot sensitivity data from CHEMKIN sensitivity spreadsheets

Tools for sensitivity analysis for weighted estimators

A unique sensitive form for web apps, much more responsive on PC view

Recall & Precision results of the UTA7 statistical analysis.

Supervised Machine Learning and Credit Risk

Functions for Medical Decision Making for ClinicoPath jamovi Module

A library for working with Windows 10, April 2018 Update's addition of per-directory case sensitivity.

Here we will be firstly analysing the how different threshold values effect the area under the Curve in a Receiver Operating charcteristic(ROC) curve. And at last we will show how to define a function in python to calculate the most optimal threshold value for the logistic Regression.

The project involves analyzing certain issues of customer churn faced by telecom companies. Models are required to be built so as to predict whether a customer will cancel their service in the future or not and then model comparison measures are made for taking interpretation and recommendations from the best model.

To predict which customer is most likely to convert

Predicting Heart Disease using Machine Learning