/feature-selection-notebook

Feature selection for machine learning using mutual information.

Primary LanguageJupyter NotebookOtherNOASSERTION

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Feature Selection Jupyter Notebook

This notebook develops a QPU programming model for an optimization problem that selects a subset and demonstrates it using Ocean software's dwave-hybrid on an example of feature selection for machine learning.

The notebook has the following sections:

  1. What is Feature Selection? defines and explains the feature-selection problem.
  2. Feature Selection by Mutual Information describes a particular method of feature selection that is demonstrated in this notebook.
  3. Solving Feature Selection on a Quantum Computer shows how such optimization problems can be formulated for solution on a D-Wave quantum computer.
  4. Example Application: Predicting Survival of Titanic Passengers demonstrates the use of Kerberos, an out-of-the-box classical-quantum hybrid sampler, to select optimal features for a public-domain dataset.

What is Feature Selection?

Statistical and machine-learning models use sets of input variables ("features") to predict output variables of interest. Feature selection can be part of the model design process: selecting from a large set of potential features a highly informative subset simplifies the model and reduces dimensionality.

For systems with large numbers of potential input information—for example, weather forecasting or image recognition—model complexity and required compute resources can be daunting. Feature selection can help make such models tractable.

However, optimal feature selection can itself be a hard problem. This example introduces a powerful method of optimizing feature selection based on a complex probability calculation. This calculation is submitted for solution to a quantum computer.

Example Solution

Installation

You can run this example in the Leap IDE.

Alternatively, install requirements locally (ideally, in a virtual environment):

pip install -r requirements.txt

Usage

To enable notebook extensions1:

jupyter contrib nbextension install --sys-prefix
jupyter nbextension enable toc2/main
jupyter nbextension enable exercise/main
jupyter nbextension enable exercise2/main
jupyter nbextension enable python-markdown/main

To run the notebook:

jupyter notebook

License

See LICENSE file.

Footnotes

  1. Leap's IDE, which runs VS Code, does not support all notebook extensions. ↩