Description: Explanatory analysis is an analytical approach that seeks to unravel and clarify the relationships between various variables within a dataset. Unlike other analytical methods that may focus on prediction or classification, explanatory analysis emphasizes understanding the ‘why’ behind observed patterns. This type of analysis is fundamental in various analytical contexts, including unsupervised learning, where there are no predefined labels for the data, forcing analysts to explore interrelationships and formulate hypotheses about the underlying causes of observed phenomena. In the context of explainable artificial intelligence, explanatory analysis becomes a crucial tool for interpreting complex models, allowing researchers and users to understand how and why a model makes specific decisions. This is especially relevant in applications where transparency and trust are essential, such as in healthcare or finance. In summary, explanatory analysis not only provides deeper insight into data but also fosters greater trust in artificial intelligence systems by making their decision-making processes more accessible and understandable to humans.
