Contents
What is Quest algorithm?
QUEST is proposed by Loh and Shih (1997), and stands for Quick, Unbiased, Efficient, Statistical Tree. It is a tree-structured classification algorithm that yields a binary decision tree. A comparison study of QUEST and other algorithms was conducted by Lim et al (2000).
Why clustering method is more suitable than decision tree method?
Decision trees are appropriate when there is a target variable for which all records in a cluster should have a similar value. Records in a cluster will also be similar in other ways since they are all described by the same set of rules, but the target variable drives the process.
Can decision tree can be used for clustering?
Can decision trees be used for performing clustering? Decision trees can also be used to for clusters in the data but clustering often generates natural clusters and is not dependent on any objective function.
What are the advantages of a decision tree algorithm?
A decision tree algorithm can handle both categorical and numeric data and is much efficient compared to other algorithms. Any missing value present in the data does not affect a decision tree which is why it is considered a flexible algorithm. These are the advantages.
How does quest compare to other decision trees?
Why is it (apparently) not in as widespread use as C&RT or C5.0? QUEST stands for Quick, Unbiased and Efficient Statistical Tree. It uses ANOVA F and contingency table Chi Square tests to select variables for splitting.
Why are decision trees used in machine learning?
Every machine learning algorithm has its own benefits and reason for implementation. Decision tree algorithm is one such widely used algorithm. A decision tree is an upside-down tree that makes decisions based on the conditions present in the data. Now the question arises why decision tree?
How is distribution of records done in a decision tree?
Distribution of records is done in a recursive manner on the basis of attribute values. Different libraries of different programming languages use particular default algorithms to build a decision tree but it is quite unclear for a data scientist to understand the difference between the algorithms used. Here we will discuss those algorithms.