• Streamlining workflows with pipelines
  • Loading the Breast Cancer Wisconsin dataset
  • Combining transformers and estimators in a pipeline
• Using k-fold cross-validation to assess model performance
  • The holdout method
  • K-fold cross-validation
• Debugging algorithms with learning and validation curves
  • Diagnosing bias and variance problems with learning curves
  • Addressing over- and underfitting with validation curves
• Fine-tuning machine learning models via grid search
  • Tuning hyperparameters via grid search
  • Exploring hyperparameter configurations more widely with randomized search
  • More resource-efficient hyperparameter search with successive halving
  • Algorithm selection with nested cross-validation
• Looking at different performance evaluation metrics
  • Reading a confusion matrix
  • Optimizing the precision and recall of a classification model
  • Plotting a receiver operating characteristic
  • Scoring metrics for multiclass classification
• Dealing with class imbalance
• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.