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+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+##  Chapter 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
+
+- 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
+