# Should we use discrete or continuous input for decision trees

I have 2 datasets, a continuous dataset(75 datapoints and 14 variables) and a discretized dataset which was made by placing the continuous datasets into buckets. I have built a decision tree classifier (using the python sklearn package) and the classifier works much better for the discrete dataset rather than the continuous dataset.

I have also read in a few papers that sometimes it is preferable to use discrete datasets. But I don't know why. I Would appreciate any input or an explanation.