I am currently studying about decision tree while studying I came across two parameters in the decision tree which can improve the performance of the model.
the cp parameter determines when the splitting up of the decision tree stops.
the minsplit parameter monitors the amount of observations in a bucket. If a certain threshold is not reached any further splitting can be done.
But I am not able to understand how they affect the model and how to use them to improve the performance of the model.