mutual-information-feature-selection

The Mutual Information QUBO (MIQUBO) tool provides a method for feature selection by utilizing mutual information to identify an optimal subset of features for predicting outcomes, specifically applied to Titanic passenger survival data. By formulating the problem as a quadratic unconstrained binary optimization (QUBO), it leverages D-Wave's quantum computing capabilities to solve complex feature selection tasks efficiently, while focusing on reducing redundancy among selected features. Notably, the tool addresses a challenging calculation that scales exponentially with the number of features, making it suitable for high-dimensional datasets.