about.txt

Machine Learning Classifier Comparison Tool

Version: 0.2
Author: Alice Williams
Organization: Visual Knowledge Discovery and Imaging Lab
Location: Central Washington University
License: MIT

Description:
This tool allows you to compare multiple machine learning classifiers on your dataset using various evaluation metrics. It supports both cross-validation and direct evaluation on a secondary dataset.

Features:
- Support for 21 different classifiers from scikit-learn and other popular libraries
- Configurable hyperparameters for each classifier
- Cross-validation with customizable splits
- Multiple evaluation metrics (Accuracy, F1-score, Recall)
- Interactive parallel coordinates visualization
- Export results to CSV
- Support for both training and evaluation datasets
- Visualization of the results in a parallel coordinates plot

Classifiers Supported:
- Numerical Data: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression, Ridge Classifier, Stochastic Gradient Descent (SGD) Classifier
- Binary Data: Bernoulli Naive Bayes
- Categorical Data: Decision Trees, Random Forests, Extra Trees Classifier, Multinomial Naive Bayes
- Mixed Data Types: Gradient Boosting Classifier, Extreme Gradient Boosting (XGBoost), AdaBoost Classifier, Light Gradient Boosting Machine (LightGBM), CatBoost Classifier, Passive Aggressive Classifier, Perceptron

Libraries Used:
- scikit-learn
- pandas
- numpy
- matplotlib
- xgboost
- lightgbm
- catboost

For more information about the classifiers and their parameters, 
refer to the scikit-learn documentation:
https://scikit-learn.org/stable/supervised_learning.html