Compare classifier performance on datasets
Explore and compare LLM models through interactive leaderboards and submissions
Explore speech recognition model performance
Generate detailed data profile reports
A Leaderboard that demonstrates LMM reasoning capabilities
Execute commands and visualize data
Analyze and visualize car data
Display CLIP benchmark results for inference performance
Generate a data report using the pandas-profiling tool
Generate benchmark plots for text generation models
This is AI app that help to chat with your CSV & Excel.
Analyze data to generate a comprehensive profile report
Analyze your dataset with guided tools
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.