Python Machine Learning - Code Examples

Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Chapter Outline

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
- 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

A note on using the code examples

The recommended way to interact with the code examples in this book is via Jupyter Notebook (the .ipynb files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.

Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:

conda install jupyter notebook

Then you can launch jupyter notebook by executing

jupyter notebook

A window will open up in your browser, which you can then use to navigate to the target directory that contains the .ipynb file you wish to open.

More installation and setup instructions can be found in the README.md file of Chapter 1.

(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: ch06.ipynb)

In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.

When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (.py files) that can be viewed and edited in any plaintext editor.