Machine Learning
Evaluation Metrics & Advanced Techniques for Imbalanced Data
This course focuses on evaluating models with imbalanced data, and explores advanced techniques including cost-sensitive learning, ensemble methods, and anomaly detection for extreme imbalance. You'll learn appropriate evaluation strategies specifically designed for imbalanced datasets, helping you choose the right metrics and avoid common pitfalls.
Pandas
Python
Scikit-learn
4 lessons
18 practices
2 hours
Model Validation and Selection
Course details
Comprehensive Evaluation Strategies for Imbalanced Data
Beyond Accuracy Metrics for Imbalanced Data
Extracting Insights from Confusion Matrices
Fixing Parameter Order in Evaluation Metrics
Generating Classification Reports for Imbalanced Data
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal
