Sebastian Raschka, 2015

Python Machine Learning - Code Examples

• 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