Python Machine Learning - Code Examples
- Learning with ensembles
- Combining classifiers via majority vote
- Implementing a simple majority vote classifier
- Using the majority voting principle to make predictions
- Evaluating and tuning the ensemble classifier
- Bagging – building an ensemble of classifiers from bootstrap samples
- Bagging in a nutshell
- Applying bagging to classify samples in the Wine dataset
- Leveraging weak learners via adaptive boosting
- How boosting works
- Applying AdaBoost using scikit-learn
- Summary
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: ch07.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.