How to make predictions with a decision tree on a dataset without a target value?
Decision tree is a supervised algorithm. That means you must use some target value(or lable) to build the tree(dividing node's value based on information gain rule).
Every tutorial I have found about machine learning includes testing an algorithm on a dataset that has target values and then it finds how accurate the algorithm is by testing its predictions on the test set.
What if you then receive all of the data except for the target value and you want to make target value predictions to see if they come true in the future?Every tutorial I have seen has been with data that they already know the future target value predictions.