Attributewise

Attributewise is a term used in data analysis and artificial intelligence to describe operations or considerations that focus on individual attributes or features within a dataset. Instead of analyzing the entire record or entity as a whole, an attributewise approach examines each attribute independently or in relation to other specific attributes. This method is often employed when identifying the importance of a particular feature, understanding its distribution, or when designing models that process features separately.

For instance, in machine learning, an attributewise feature selection process might evaluate each attribute's correlation with the target variable or its statistical significance. Similarly, data cleaning might involve checking the validity or range of values for each attribute individually. This contrasts with a holistic approach that might consider the interaction between multiple attributes simultaneously from the outset.

The benefits of an attributewise perspective include clarity in understanding the role of each component of the data and computational efficiency in certain analytical tasks. However, it can sometimes overlook complex interdependencies between attributes that might be crucial for a complete understanding or for building highly accurate predictive models. Therefore, a balanced approach often combines attributewise analysis with multivariate or interaction-based methods.