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 overfitting and underfitting with validation curves
Fine-tuning machine learning models via grid search
  Tuning hyperparameters via grid search
  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
  The scoring metrics for multiclass classification
Summary