Chapter 5 in the caret doco covers it in great detail. At this point we could successfully unleash the dogs of war sorry Shakespeare and train our model since we know we want to use chaid. Turns out in this case the best solution was what chaid uses as defaults. The very last line of the output tells us that. Resampling results across tuning parameters: alpha2 alpha4 Accuracy Kappa 0. The output gives us a nice concise summary.
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Chapter 5 in the caret doco covers it in great detail. At this point we could successfully unleash the dogs of war sorry Shakespeare and train our model since we know we want to use chaid. Turns out in this case the best solution was what chaid uses as defaults. The very last line of the output tells us that. Resampling results across tuning parameters: alpha2 alpha4 Accuracy Kappa 0. The output gives us a nice concise summary. It gives us an idea of how many of the cases were used in the individual folds Summary of sample sizes: , , , , , , The bit about alpha2, alpha4, and alpha3 is somewhat mysterious.
But it is clear that it thought Kappa of 0. But what about the things we were used to seeing? Well if you remember that caret is reporting averages of all the folds it sort of makes sense that the best final model results are now in chaid.
Look in chaid. By default caret allows us to adjust three parameters in our chaid model; alpha2, alpha3, and alpha4. As a matter of fact it will allow us to build a grid of those parameters and test all the permutations we like, using the same cross-validation process. The function in caret is tuneGrid. Some key points here. Even though our model got more conservative and has far fewer nodes, our accuracy has improved as measured both by traditional accuracy and Kappa.
That applies at both the average fold level but more importantly at the best model prediction stage. The plot is also more useful now. No matter what we do with alpha2 it pays to keep alpha4 conservative at. This goes to the heart of our conversation about over-fitting. Earlier I printed the results of chaid. Under the covers one of the strengths of caret is that it keeps some default information about how to tune various types of algorithms.
They are visible at My experience is that they are quite comprehensive and allow you to get your modelling done. But sometimes you want to do something your own way or different and caret has provisions for that.
If you look at the default model setup for CHAID here on you can see that it only allows you to tune on alpha2, alpha3, and alpha4 by default. What if, for example, we wanted to tune based upon minsplit, minbucket, minprob, maxheight instead? How would we go about using all the built in functionality in caret but have it our way?
R Decision Trees – The Best Tutorial on Tree Based Modeling in R!
We will build these trees as well as comprehend their underlying concepts. We will also go through their applications, types as well as various advantages and disadvantages. Decision Trees are a popular Data Mining technique that makes use of a tree-like structure to deliver consequences based on input decisions. One important property of decision trees is that it is used for both regression and classification. This type of classification method is capable of handling heterogeneous as well as missing data. Decision Trees are further capable of producing understandable rules.
CHAID vs. ranger vs. xgboost — a comparison
CHAID and caret – a good combo – June 6, 2018