- 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.