Test your understanding of essential concepts in model evaluation, including train/validation/test splits, cross-validation, choosing metrics like accuracy, precision, recall, F1, and ROC-AUC, as well as methods to prevent overfitting and data leakage. This quiz helps you assess your knowledge on best practices for building robust machine learning models.

Test your understanding of feature preprocessing techniques and data pipeline best practices, including handling missing values, encoding categorical variables, scaling, and ensuring reproducible workflows. This quiz covers practical scenarios and concepts essential for building robust, efficient machine learning pipelines.

Test your understanding of machine learning pipeline essentials, including train, validation, and test splits, the principles of cross-validation, and best practices to prevent data leakage. Ideal for learners aiming to build strong foundational skills in model evaluation and reliable machine learning workflows.

Test your understanding of how to choose and interpret evaluation metrics for classification and regression, with a special focus on threshold tuning for imbalanced data. This quiz will help you grasp key concepts essential for model evaluation and improvement.