What is cart and CHAID?

What is cart and CHAID?

CART stands for classification and regression trees where as CHAID represents Chi-Square automatic interaction detector. A key difference between the two models, is that CART produces binary splits, one out of two possible outcomes, whereas CHAID can produce multiple branches of a single root/parent node.

What is a cart analysis?

CART analysis is used in data exploration to classify systems that differ due to natural causes. CART analysis may be used to determine the relative importance of different variables for identifying homogeneous groups within the data set.

What does chaid stand for?

Chi-square automatic interaction detection (CHAID) is a decision tree technique, based on adjusted significance testing (Bonferroni testing). The technique was developed in South Africa and was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on this topic.

How do you do chaid analysis?

Building the CHAID Tree Model

1. To run a Decision Tree analysis, from the menus choose: Analyze > Classify > Tree…
2. Select Credit rating as the dependent variable.
3. Select all the remaining variables as independent variables.

What is use of CART algorithm?

The CART algorithm works to find the independent variable that creates the best homogeneous group when splitting the data. For a classification problem where the response variable is categorical, this is decided by calculating the information gained based upon the entropy resulting from the split.

Is CHAID a word?

CHAID is a type of decision tree technique, based upon adjusted significance testing ( Bonferroni testing). CHAID can be used for prediction (in a similar fashion to regression analysis, this version of CHAID being originally known as XAID) as well as classification, and for detection of interaction between variables.

How is a cart model different from a CHAID model?

In a CART model, the entire tree is grown, and then branches where data is deemed to be an over-fit are truncated by comparing the decision tree through the withheld subset. CHAID uses a statistical rule to stop tree growth called the Chi-Square test. The Chi-Square test qualifies the values observed, to those in theory.

Which is better a cart or a CHAID decision tree?

There are a lot of pros and cons lists available to draw a comparison between CART and CHAID but I believe that each suits a different purpose. You have to work around and play with your data and experiment a lot with a variety of methods to actually get the hang of what is good for a particular business problem that you are trying to solve.

What does CHAID stand for in CART listendata?

Splitting stops when CART detects no further gain can be made, or some pre-set stopping rules are met. (Alternatively, the data are split as much as possible and then the tree is later pruned. CHAID stands for Chi-square Automated Interaction Detection.

Which is more useful for analysis, cart or CHAID?

Now I get to my point: It appears to me that CHAID is most useful for analysis, whereas CART is more suitable for prediction.