Which columns can be created when applying a machine learning model?

When applying a machine learning model, a common type of output is the "PredictedOutcome" column. This column represents the primary predictions made by the model based on the input features. For instance, in a classification problem, this column could indicate the class label or category assigned to each instance based on the model's learned patterns and algorithms.

Generating the "PredictedOutcome" is a fundamental aspect of machine learning applications, as it directly reflects the model’s predictions, thereby providing essential insights and enabling decision-making based on the analysis of the underlying data patterns.

Other options, while potentially relevant in specific contexts, are less universally applicable across all machine learning implementations. Predictive totals, confidence percentages, and prediction groups can also be derived from a machine learning model, but they are often additional metrics or derived data points that complement the main predictions, rather than being fundamental outputs of the initial modeling process.