Get an intuitive sense for the ROC curve and other binary classification metrics with interactive visualization.
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Updated
Dec 29, 2024 - Python
Get an intuitive sense for the ROC curve and other binary classification metrics with interactive visualization.
Matthews Correlation Coefficient Loss implementation for image segmentation.
This repository contains a MATLAB-based Machine Learning Software (MLS) offers advanced biomedical signal processing with an intuitive GUI for analyzing EEG, ECG, and EMG. Features include noise filtering, feature extraction, dimensionality reduction, and customizable machine learning algorithms for tailored classification and analysis.
Short, readable equations predicting the MCC a classifier reaches on a dataset, fitted from meta-features of both by sparse regression over a fixed term library. Validated leave-one-dataset-out and leave-one-model-out; trades accuracy for explainability on purpose.
Emotion Analysis with Transformers
Implementation of some basic Image Annotation methods (using various loss functions & threshold optimization) on Corel-5k dataset with PyTorch library
Nonparametric comparison of convolutional neural networks and transformers to classify COVID-19
Comprehensive Object-Oriented Programming Python implementation of a machine learning pipeline for diabetes prediction, featuring nested cross-validation, Bayesian hyperparameter optimization, and robust preprocessing for accurate and reliable outcomes.
Duplicate-safe, tie-aware replication package for SoftMCC validation ranking under class imbalance
Replication package for streaming SoftMCC estimators under concept drift
Official Pytorch Implementation of: "Improving Loss Function for Deep CNN-based Automatic Image Annotation"
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