Decision trees are useful for relatively small datasets that have a relatively simple underlying ... the data into two sets in such a way that the most information is obtained. After the first split, ...

Starting with all 200 training items, the decision tree algorithm scans the data and finds the one value of the one predictor variable that splits the data into two sets in such a way that the most ..

Four years of research led to a specific decision tree data mining algorithm yielding best results ... Produce a set of clear criteria easy to understand by exploration staff.

Decision trees are a simple but powerful prediction method ... Next, we divide the input data set into training and test sets in k different ways to generate different trees.

Decision trees ... because they combine simple questions about the data in an understandable way. Approaches for extracting decision rules from decision trees have also been successful 1.

What are the advantages of logistic regression over decision ... algorithms can cross - meaning, logistic performs better on a small version of the dataset but eventually is beaten by the tree ...

Describing a decision-making system as an “algorithm” is often a way to deflect accountability ... It was more akin to a very simple formula or decision tree designed by a human committee.