Platform empowering data science professionals with comprehensive tools to evaluate classifier performance in imbalanced classification tasks
accurate performance evaluation metrics
visualization tools for classifier performance
benchmarks for imbalanced data
8×
5-Yr Growth
Medium
AI Confidence
Comprehensive suite of features including visualizations of confusion matrices, ROC-AUC, and AUC-PR curves, plus benchmarks and guidelines for acceptable false positive rates for imbalanced datasets
accurate performance evaluation metrics
visualization tools for classifier performance
benchmarks for imbalanced data
decision-making support for model optimization
AI confidence: medium
Year 1
500 users
Year 3
3K users
Year 5
6K users
A multi-channel strategy focused on sustainable, compounding growth.
people working on imbalanced classification tasks in data science struggling with determining the success of a classifier and deciding on an acceptable number of false positives
Comprehensive suite of features including visualizations of confusion matrices, ROC-AUC, and AUC-PR curves, plus benchmarks and guidelines for acceptable false positive rates for imbalanced datasets
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