Learning to Split for Automatic Bias Detection

Reproducible archetype-based train/test splitting and benchmarking for ML data sets

`Spltr` is a simple PyTorch-based data loader and splitter. It may be used to load arrays and matrices or Pandas DataFrames and CSV files containing numerical data with subsequent split it into train, test (validation) subsets in the form of PyTorch DataLoader objects.

Machine Learning Class - ECE - TTU - Spring 2020

This program obtains orginal set file and randomly seperate it into train_val or test set file line by line.

Parkinson's Disease Prediction using Machine Learning with a web-based interface built with React, Flask, and Node.js for real-time predictions and user history tracking.

A simple PyTorch-based neural network that classifies student exam outcomes (Pass/Fail) using study hours and previous exam scores. Implements dataset splitting (train/val/test), mini-batch training, and evaluation with configurable hyperparameters.

This project predicts loan approval outcomes (Approved/Rejected) using a PyTorch neural network. It includes data preprocessing, train/validation/test split, model training with BCEWithLogitsLoss, and inference with probability-based classification.

Apply DUPLEX data split to the given dataset and return training and test datasets. REF: Snee, R. D. (1977). Validation of regression models: methods and examples. Technometrics, 19(4), 415-428.