Machine Learning Model Evaluation Metrics with Scikit-learn

Evaluating machine learning models correctly is essential for building reliable data science applications. Selecting the wrong evaluation metric can hide serious model flaws and lead to poor decision-making in real-world scenarios. This written course provides a thorough foundation in evaluation techniques for both classification and regression tasks using Python and Scikit-learn. What you'll learn: - Understand foundational concepts of model validation, error types, and baseline benchmarks - Calculate and interpret classification metrics including precision, recall, F1-score, and ROC-AUC - Measure regression accuracy using MSE, RMSE, MAE, R-squared, and adjusted metrics - Handle imbalanced datasets effectively with specialized evaluation strategies and PR curves - Apply cross-validation techniques to prevent overfitting and assess model generalizability - Implement custom evaluation scoring functions within Scikit-learn pipelines The course begins with key terminology, basic principles, and mathematical intuitions behind each metric before walking through practical code examples. You will read structured explanations and analyze practical code implementations step by step. This course is designed for beginner data scientists, machine learning enthusiasts, and data analysts who want to master model evaluation. Basic familiarity with Python programming is recommended.