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model-evaluation-metrics

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text-to-image-eval

Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.

  • Updated Jul 29, 2024
  • Jupyter Notebook

This repository contains a project showcasing Federated Learning using the EMNIST dataset. Federated Learning is a privacy-preserving machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.

  • Updated Sep 24, 2023
  • Jupyter Notebook

Successfully established a machine learning model which can determine whether an individual is vulnerable to the Cirrhosis disease or not by predicting its corresponding stage based on a unique set of medical features such as Cholesterol, Prothrombin, etc. pertaining to that person.

  • Updated Jun 8, 2022
  • Jupyter Notebook

This breast cancer diagnosis project evaluates various machine learning models to effectively classify breast masses as benign or malignant. SVM and Logistic Regression excel in identifying positive cases, leveraging their robust performance metrics, while Neural Networks show promising results and offer opportunities for further enhancement!

  • Updated Jun 21, 2024
  • Jupyter Notebook

A spam detection model built to handle imbalanced data using small pipelines. This project walks through text preprocessing, model tuning, and performance evaluation with ROC-AUC curves and classification reports, focusing on practical steps like using XGBoost and TFIDF for spam classification.

  • Updated Sep 7, 2024
  • Jupyter Notebook

🗣️ Speech Type Detection is a Flask app to classifies text into categories like "Hate Speech," "Offensive Language," or "No Hate or Offensive Language" with 87.3% accuracy. It offers a user-friendly interface for text input and prediction, using machine learning algorithms. Idea for managing online inappropriate language. 🌐🔍.

  • Updated Jun 5, 2024
  • Python

This project utilizes machine learning to address the broad problem of spam through algorithms like Multinomial Naive Bayes and Logistic Regression; it can classify incoming emails as either spam or ham. This project aims to enhance email security and user experience while minimizing the risks of phishing attacks.

  • Updated Oct 30, 2024
  • Jupyter Notebook

Projeto que utiliza a base de dados Iris para calcular a acurácia e a função de perda de um modelo de aprendizado de máquina. Focado em análise de desempenho e avaliação de modelos.

  • Updated Sep 30, 2024
  • C

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