Predictive Modeling Platforms
Platforms that combine multiple approaches for predictive modeling and forecasting.
Supported Solution Fields
When to Use
- When you need multiple modeling approaches
- When you want automated model selection
- When you need enterprise features
When Not to Use
- When you need a specific algorithm
- When you want full control
- When you have budget constraints
Tradeoffs
- Flexibility vs Cost: More features mean higher cost
- Automation vs Control: Less manual intervention needed
- Power vs Complexity: More powerful features require more expertise
Commercial Implementations
-
DataRobot
- AutoML capabilities
- Time series focus
- Enterprise deployment
-
H2O.ai
- Open source core
- Automated forecasting
- Scalable deployment
Common Combinations
- Business intelligence
- Risk analysis
- Operations planning
Case Study: Business Forecasting
A corporation implemented automated predictive modeling:
Challenge
- Multiple business metrics
- Various prediction horizons
- Complex dependencies
Solution
- Implemented DataRobot
- Automated model selection
- Integrated business rules
Results
- 40% faster modeling
- Improved accuracy