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 ideal for data that is uniformly distributed across its range. It is particularly effective when a straightforward and quick discretization approach is needed without the necessity to closely examine the data distribution.

• Equal-frequency binning ensures that each bin contains roughly the same number of data points, prioritizing distribution equality over bin width uniformity. This approach is adept at handling skewed data and outliers, as it allocates an equal number of observations to each bin, offering a balanced representation of the data's distribution. It is best utilized when dealing with non-uniform data distributions, aiming to make models more sensitive to the actual distribution of data by ensuring uniform observation counts across bins.

Choosing between equal-width and equal-frequency binning depends on the specific requirements of your analysis or model and the characteristics of your data. Equal-width binning is straightforward and may be more appropriate for evenly distributed data. In contrast, equal-frequency binning is often better suited for data with outliers or a skewed distribution, as it ensures that each bin has a similar number of data points, leading to potentially more meaningful and balanced analysis.

Diabetes scenario

Let's consider a Bayesian network designed to predict the likelihood of a person having diabetes based on their age, body mass index (BMI), and blood pressure. In this example, all three input variables (age, BMI, and blood pressure) are continuous.

Training the network by discretizing the data

To discretize the data and create the Bayesian network using CausalNex, we follow these steps:

First, let's import the necessary libraries and create simulated data: