What defines how the model should utilize raw input data?

Feature columns play a critical role in defining how a model utilizes raw input data. They serve as a structured representation of the raw data, transforming it into a format that the model can effectively understand and learn from. Feature columns allow you to specify which attributes or properties of the raw data should be used as inputs for training the model and how they should be treated, such as by indicating categorical versus numeric features.

In many machine learning frameworks, feature columns encapsulate preprocessing steps like encoding categorical data, normalizing numerical values, or deriving new features from existing ones. This encapsulation simplifies the process of preparing data for modeling, as it allows for a more streamlined and efficient workflow, enabling the model to focus on learning from relevant features rather than raw and potentially unstructured data.

Understanding feature columns is essential for effective feature engineering and, consequently, model performance. They directly influence the model’s input layer, guiding how data is formatted, scaled, or processed before it enters the model architecture. Thus, they are foundational in shaping how raw input data is interacted with during the learning process.