Compare classifier performance on datasets
Generate plots for GP and PFN posterior approximations
Leaderboard for text-to-video generation models
Life System and Habit Tracker
Cluster data points using KMeans
Evaluate model predictions and update leaderboard
Analyze and visualize data with various statistical methods
Generate detailed data reports
Calculate VRAM requirements for running large language models
VLMEvalKit Evaluation Results Collection
Embed and use ZeroEval for evaluation tasks
Open Agent Leaderboard
Generate detailed data profile reports
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.