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