Calculating model accuracy is a critical part of any machine learning project yet many data science tools make it difficult or impossible to assess the true accuracy of a model. Often tools only validate the model selection itself, not what happens around the selection. Or worse, they don’t support tried and true techniques like cross-validation.
This whitepaper discusses the four mandatory components for the correct validation of machine learning models, and how correct model validation works inside RapidMiner Studio.
Related Resources. Take a Look!
Gartner Magic Quadrant for Data Science and Machine Learning Platforms
Get a complimentary copy of the 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.
Model Accuracy Isn’t Enough: You Need Resilient Models
Resilience is the new accuracy in data science projects. Here’s why your “best” model might not be the best at all…
Talking Value: Optimizing Enterprise AI with Profit-Sensitive Scoring
Don’t just make the best data science decision, make the best business decision. Learn how to create a confusion matrix and better understand your model’s results.