Discretize Data to Feed the Bayesian Network

Discretization is the process of converting continuous variables into discrete categories or bins. This technique involves dividing the range of a continuous variable into a finite number of intervals, and then assigning each data point to a specific interval or category. Discretization simplifies the data and makes it more manageable for certain types of models, like Bayesian networks, which often perform better with categorical data. By categorizing continuous data, discretization helps in reducing model complexity, enhancing interpretability, and often improving the model's performance by reducing the effects of minor observation errors or noise in the data.

Equal-width binning and equal-frequency binning are two common methods for discretizing continuous variables, but they approach the task differently:

• Equal-width binning combines simplicity and interpretability by dividing the range of a continuous variable into intervals of the same width, resulting in bins of uniform size but potentially varying data point counts. This method shines with its ease of understanding and implementation, making it ...