Release Date: 2019-06-26
Try out the new Feature Selection Jupyter Notebook, which demonstrates the formulation of an n-choose-k optimization problem for solution on a D-Wave quantum computer. The method used in the example problem of this notebook—feature-selection for machine learning— is applicable to problems from a wide range of domains; for example, financial portfolio optimization.
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.
Note: This notebook includes an example that uses a hybrid sampler, so not only is it a good introduction to D-Wave system's utility in machine learning, but also it is a great way to get started with D-Wave Hybrid.
Access this and other notebooks here.