3Modeling

• 3.1Choosing Algorithms

  Match the model to the task—understand supervised (classification/regression), unsupervised (clustering/dimensionality reduction), SageMaker built-in algorithms, and specialized tasks (images/text/time series).

• 3.2Training, Overfitting, and Hyperparameter Tuning

  Train well—understand overfitting/underfitting, bias/variance, regularization/early stopping, hyperparameter tuning (SageMaker Automatic Model Tuning), and cross-validation.

• 3.3Model Evaluation Metrics

  Measure quality—understand the confusion matrix, precision/recall/F1, ROC-AUC, regression metrics (RMSE/MAE/R²), and metric choice under imbalance. Evaluate with the right metric.