R package 'wROC'. Estimation of the ROC curve and AUC with complex survey data.
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Updated
Sep 19, 2024 - R
R package 'wROC'. Estimation of the ROC curve and AUC with complex survey data.
ROC-GLM and calibration analysis for DataSHIELD
Car insurance prediction using Logistic Regression and the Naive Bayes
PyCaret: Simplifying machine learning workflows with a low-code, open-source Python library.
This project employs XGBoost regression and XGBoost classifier model to predict user order and user churn on online travel agency data. Reach 97% prediction accuracy.
Scoring model for financial company - all files
ROC-GLM for DataSHIELD
Raisin Class Prediction
Práctica de clasificación con Machine Learning en el dataset del Titanic, abordando exploración de datos, preprocesamiento, selección de métricas y modelos, con el objetivo de analizar detalladamente los resultados obtenidos.
run a multitude of classifiers on you data and get an AUC report
Evaluation of supervised predictions for two-class and multi-class classifiers
Capstone Project for Insurance Premium Default Propensity
Leverage Supervised Machine-learning Techiques to Predict Diabetes from Blood Test
Classification and Regression Performance Metrics library
Three fast ROC AUC calculation implementations for python
This repository includes a multi-task learning example on the face images to classify the genders and races of people. The dataset is from the Kaggle.
R library - A binary classifier based on the class probability at a given rank following Fermi-Dirac distribution
In this project, I use 3 machine learning models (CART, Random Forest and ANN) to predict the claim frequency for a travel insurance firm. I also evaluate which of the three models is most suitable for our dataset.
An intelligent video surveillance system also provides video cameras and recording solutions to monitor every part of the building and site perimeter. Yet it also utilizes smart security technology such as sensors and AI models.
We classify whether the data in the dataset is churn or not
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