Compare classifier performance on datasets
Display server status information
This project is a GUI for the gpustack/gguf-parser-go
Search for tagged characters in Animagine datasets
Need to analyze data? Let a Llama-3.1 agent do it for you!
Parse bilibili bvid to aid / cid
Generate images based on data
Explore and filter model evaluation results
Generate synthetic dataset files (JSON Lines)
Generate detailed data reports
NSFW Text Generator for Detecting NSFW Text
Create detailed data reports
Monitor application health
Classification is a supervised learning technique used to predict the category or class of an object or data point based on its features. It is a fundamental task in machine learning where models are trained on labeled data to classify new, unseen data into predefined categories. The Classification tool allows users to compare the performance of different classifiers on various datasets, providing insights into which algorithm works best for specific use cases.
• Multiple Classifier Support: Test and compare performance across different classification algorithms. • Dataset Flexibility: Works with diverse datasets from various domains. • Performance Metrics: Provides detailed accuracy, precision, recall, and F1-score for each classifier. • Visual Comparison: Presents results in a clear, understandable format for easy analysis. • Customizable Settings: Allows users to tweak parameters for specific use cases. • Export Results: Quickly export analysis for reports or further processing.
What is classification used for?
Classification is used for predicting categories or classes in data. Common applications include spam detection, sentiment analysis, and medical diagnosis.
What classifiers are supported?
Common classifiers like logistic regression, decision trees, random forests, and SVMs are typically supported.
How do I handle imbalanced datasets?
Techniques like resampling, adjusting class weights, or using algorithms robust to imbalance can help.