leave-one-out-cross-validation | Topic

Topic: "leave-one-out-cross-validation"

brainhack-school2020/abide-fmri

Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.

Language: Jupyter Notebook - Size: 42.5 MB - Last synced at: 12 months ago - Pushed at: over 1 year ago - Stars: 35 - Forks: 13

JingweiToo/Machine-Learning-Toolbox

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

Language: MATLAB - Size: 97.7 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 15 - Forks: 3

RoryQo/Tuition-Variation-Research

This project predicts tuition rates for U.S. public and private universities using linear regression with leave-one-out cross-validation. Helping to assess if a college market price, maximizing ROI and minimizing student loan debt.

Language: R - Size: 1.91 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 4 - Forks: 0

saiyedduri/Geo-statistical-analysis-on-Co-Ni-deposit

This repository aims to explore the Co-Ni deposits samples and develop a model using ordinary kriging technique. The model is further validated by leave one out cross validation strategy.

Language: R - Size: 2.24 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

SeyedMuhammadHosseinMousavi/Comprehensive-Machine-Learning-Techniques-Metrics-Classifiers-and-Evaluation

Comprehensive Machine Learning Techniques: Metrics, Classifiers, and Evaluation

Language: Python - Size: 14.8 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 1 - Forks: 0

Mashghdoust/Classifying-EEG-signals-by-extracting-features-from-a-moving-time-window-using-different-ML-models

In this project I have extarcted 30 time and frequancy features from EEG signals (of left hand and right hand moving) in an espicific time window. Then using PCA i have decreased the features dimension to 10. Then I have quarried different methdos of ML: KNN(1,3,5,6), SVM(Linear kernel, Gaussian kernel), LDA, Naive bayes on different time windows.

Language: MATLAB - Size: 12.4 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

MoinDalvs/Model_Validation_Techniques

1. train_test_split 2.K_fold 3.LeaveoneOut 4.Cross Validation Score 5.Logistic Regression

Language: Jupyter Notebook - Size: 3.91 KB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

Pradnya1208/Cross-Validation-techniques

This project aims to understand and implement all the cross validation techniques used in Machine Learning.

Language: Jupyter Notebook - Size: 1.03 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

JingweiToo/Machine-Learning-Regression-Toolbox

This toolbox offers 7 machine learning methods for regression problems.

Language: Python - Size: 49.8 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 3

vaitybharati/Model-Validation-Methods

Model-Validation-Methods

Language: Jupyter Notebook - Size: 1.95 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1

Divya171997/Residential_Properties-Applied_Statistics

The dataset contains information regarding residential properties which were collected by the US Census Service, the period 2006 to 2010.

Size: 799 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0

Ehsan-Behzadi/A-Machine-Learning-Approach-Using-the-Pima-Indians-Diabetes-Dataset

This repository features a machine learning project utilizing the Pima Indians Diabetes Dataset to predict diabetes risk. It explores data preprocessing, model training, and evaluation using techniques such as Naive Bayes and K-Nearest Neighbors (KNN) . The aim is to highlight the impact of various health factors on diabetes prediction.

Language: Jupyter Notebook - Size: 247 KB - Last synced at: 25 days ago - Pushed at: 25 days ago - Stars: 0 - Forks: 0

exoInference/loo_cv_tutorials

This project provides a tutorial on performing leave-one-out cross-validation (LOO-CV) using the Pareto-smoothed importance sampling (PSIS) approximation. The tutorial leverages the arviz package and applies these techniques to a synthetic dataset from Welbanks et al. 2023, focusing on exoplanet atmospheric analysis.

Language: Jupyter Notebook - Size: 14.5 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0