There are 0 repository under resampling-methods topic.
Spatiotemporal resampling methods for mlr3
R package cross-validation, bootstrap, permutation, and rolling window resampling techniques for the tidyverse.
Resampling tools for openPMD PIC data
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
A MATLAB package for multivariate permutation testing and effect size measurement
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
Stanford Online course STATSX0001 "Statistical Learning" follows closely the sequence of chapters in the course text "An Introduction to Statistical Learning, with Applications in R" (James, Witten, Hastie, Tibshirani - Springer 2013). Trevor Hastie Professor of Statistics and of Biomedical Data Sciences, Stanford University, and Robert Tibshirani Professor of Biomedical Data Science and Statistics, Stanford University
Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can identify the creditworthiness of borrowers.
Resampling procedure for weakly dependent stationary observations.
Constructs a continuous-time function from a list of real numbers.
Resampling procedure for weakly dependent stationary observations.
Importance of HBA1c in predictive Modeling of probability of Hospital Re-admission (CAPSTONE PROJECT)
In this project, we perform exploratory analysis on the data and try out different models to give the best results.
CLI Image Processing and Editing Suite
To Detect Early Sepsis Disease
Supervisor : Dr. Bhaswati Ganguly
For this project, I predicted credit risk with the supervised machine learning models I built and evaluated using Python.
A collection of statistical resampling methods
Python for Geosciences: Basic python, Numpy, Matplotlib, Pandas, Data analysis, Data Visualization
A small C library for computation of U-statistics
Pattern Jitter is an algorithm for generating artificial spike trains that are maximally random while preserving the smoothed firing rates and the recent spike histories in a recorded spike train.
Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we needed to employ different techniques to train and evaluate models with unbalanced classes. Jill asks us to use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling
Working through the labs of Introduction to Statistical learning with R by Hastie et al.
A short script that lays out a complete resampling approach for unbiased data analysis. Data from R's ratdat library is used for demonstration.
Analysis on the performance of machine learning resampling and classification models to identify loan applicant credit risk.
Machine learning used to predict the loan risks and classify them as healthy loans or high-risk loans.
Resampling module of AClimate
This is an ongoing project on Characterizing, calculating and understanding(the drivers of) Elevational Migration of resient birds in the southern western Ghats.
Time Series
I'll use various techniques to train and evaluate a model based on loan risk. I will use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Classify stars, galaxies, and quasars with SDSS DR16 data. Balanced dataset using resampling techniques improves AdaBoost classifier's performance, enhancing astronomical object classification accuracy.
solving problems with data analysis, hypotheses and the most used statistical tests in ecology
Instructional materials (course files) for the BBT4206 course (Business Intelligence II) using R. Topic: Resampling Methods.