In what form can raw data be used inside machine learning models?

The use of raw data inside machine learning models is typically most effective after it has been transformed into a feature vector. This process involves the representation of the original data in a structured format that captures important characteristics in a numerical way, enabling the model to understand and process it effectively.

Feature vectors are essential because most machine learning algorithms require inputs to be numerical. By converting raw data into a feature vector, you can ensure that various types of data, whether they be textual, categorical, or numerical, are represented consistently and can be fed into the learning algorithms. This transformation can involve techniques such as normalization, encoding categorical variables, or extracting relevant features from unstructured data.

In contrast, unprocessed data might include noise or irrelevant information that could negatively impact the model's performance, while only using numeric values or categorical data limits the diverse types of inputs that can be employed effectively in machine learning. By focusing on the structured feature vector format, you enhance the model's ability to learn accurately and make predictions.