Calculate survival probability based on passenger details
Calculate VRAM requirements for LLM models
Submit deepfake detection models for evaluation
Merge Lora adapters with a base model
Display LLM benchmark leaderboard and info
Benchmark AI models by comparison
View LLM Performance Leaderboard
Run benchmarks on prediction models
Explore and submit models using the LLM Leaderboard
Evaluate RAG systems with visual analytics
Display leaderboard for earthquake intent classification models
Quantize a model for faster inference
Determine GPU requirements for large language models
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.