Calculate survival probability based on passenger details
Calculate memory usage for LLM models
Display benchmark results
Explore and submit models using the LLM Leaderboard
Evaluate AI-generated results for accuracy
Measure over-refusal in LLMs using OR-Bench
Measure BERT model performance using WASM and WebGPU
Launch web-based model application
Visualize model performance on function calling tasks
Calculate VRAM requirements for LLM models
Request model evaluation on COCO val 2017 dataset
Analyze model errors with interactive pages
Search for model performance across languages and benchmarks
Titanic Survival in Real Time is a machine learning model benchmarking tool designed to predict the survival probability of passengers from the historic Titanic disaster. By analyzing various passenger details, the tool provides real-time insights into survival likelihood based on historical data and advanced algorithms.
• Real-time survival probability calculation based on input parameters
• Historical data analysis from the Titanic passenger records
• User-friendly input interface for entering passenger details
• Multiple input parameters, including age, gender, cabin class, and more
• Clear visual representation of survival probability results
• Comparative analysis to benchmark different models and algorithms
What machine learning models are used in this tool?
The tool utilizes random forests, logistic regression, and neural networks to ensure accurate predictions.
How accurate are the survival probability calculations?
The accuracy depends on the quality of historical data and the chosen model, typically ranging between 75-90% accuracy.
Can I use this tool for real-world applications?
No, it is primarily a benchmarking and educational tool for comparing machine learning models.