What is featurization in relation to machine learning?

Featurization refers to the transformation of raw data into feature vectors that can be used as input for machine learning models. This process is crucial because raw data often contains various forms and structures, and machine learning algorithms typically require numerical input to perform effectively. Featurization involves techniques that convert categorical variables into numerical formats, normalize or standardize data, handle missing values, and create new features from existing data that can better represent the underlying patterns.

For instance, if you have a dataset with dates, featurization might include extracting weekday, month, or year features, thus enabling the model to capture temporal patterns. Additionally, this step ensures that the data is in a structured format that aligns with the assumptions of the algorithms being employed. Effective featurization enhances the model's ability to learn from the data, ultimately improving performance.

Other choices, while related to the overall workflow of machine learning, do not define featurization accurately. Capturing user feedback focuses on gathering insights about model performance. Evaluating model performance involves metrics and validation techniques to assess how well a model is performing after training. Selecting algorithms pertains to the choice of the best-suited machine learning methods for a particular problem but does not include the transformation of data into a format suitable for those