Week #4 - Model Selection and Hyperparameter Tuning

Learning objectives:

• Explain the importance of model selection and hyperparameter tuning in improving machine learning model performance, including the relationship between model complexity, bias, and variance
• Apply cross-validation techniques (k-fold, stratified, time-series) to evaluate model performance and select optimal models while avoiding overfitting
• Implement automated hyperparameter tuning methods such as Grid Search, Random Search, and Bayesian Optimization to efficiently find optimal model configurations
• Compare and evaluate different models using appropriate metrics (accuracy, precision, recall, F1-score, ROC-AUC) to make informed decisions about model selection based on the specific problem requirements and constraints
• Understand and apply regularization techniques (L1, L2, Elastic Net) to control model complexity and prevent overfitting during the model selection process

Laboratory

https://classroom.github.com/a/NhRt73hM