ch04

Python Machine Learning - Code Examples

Chapter 4: Building Good Training Datasets – Data Preprocessing

Chapter Outline

• Dealing with missing data
  • Identifying missing values in tabular data
  • Eliminating training examples or features with missing values
  • Imputing missing values
  • Understanding the scikit-learn estimator API
• Handling categorical data
  • Nominal and ordinal features
  • Creating an example dataset
  • Mapping ordinal features
  • Encoding class labels
  • Performing one-hot encoding on nominal features
• Partitioning a dataset into separate training and test sets
• Bringing features onto the same scale
• Selecting meaningful features
  • L1 and L2 regularization as penalties against model complexity
  • A geometric interpretation of L2 regularization
  • Sparse solutions with L1 regularization
  • Sequential feature selection algorithms
• Assessing feature importance with random forests
• 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: ch04.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.