I am new to predictive analytics and machine learning. I have been looking at alot of use cases online and via KNIME on classification algorithms eg Random Forest. What I have noticed in alot of the sample data is that the records already come classified. An example would be KNIME’s churn prediction model using decision trees. In the sample data there is already a field classifying the customer as one who would churn so why would we run this through a model when we already know the outcome of which customers have churned or not. I have seen the same scenario with sample datasets online where they already classify the data. In short I am not understanding why the datasets already come classified and why do we run them through a classification model if we already know the outcome?