What tool typically ignores decision processing data?

The tool that typically ignores decision processing data is the AutoNeural. Neural networks, including AutoNeural, focus primarily on understanding the relationships in the input data through complex mathematical structures, effectively bypassing explicit rules about decision-making processes. Instead of relying on predefined decision logic or criteria, AutoNeural employs a network of interconnected nodes to identify patterns in the training data, making it less dependent on how decisions were historically processed or made.

In contrast, other options like regression, neural networks (excluding AutoNeural), and decision trees use different approaches to analyze data. Regression models assume a linear or polynomial relationship to predict outcomes based on specific predictors, while decision trees explicitly define rules and conditions based on the data to make predictions. Although neural networks can also process data and find patterns like AutoNeural, they generally incorporate decision-making elements within their structure. Thus, the structure and operational philosophy of AutoNeural distinctly focus away from the traditional decision processing data, enabling it to model complex relationships in a more abstract manner.