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Can We Obtain Dependency Plots for Single CART® Trees?

The short answer is YES such plots can be generated. Historically, we concluded that such graphs would normally not be that interesting as they would frequently be single step functions reflecting the fact that individual variables often appear only once or twice in a tree. Also, such graphs would not properly reflect the effect of a varible across most of its range of values. Thus, as of SPM® 7.0 CART® does not offer such plots. However, we can see what such plots would look like by using TreeNet® to grow a one-tree model. To do this, just set up a normal model, choose the TreeNet analysis method, and set the number of trees to be grown to 1 (see green arrow below).

CART Classification And Regression Trees®

CART

CART® - Classification and Regression Trees

Ultimate Classification Tree:

Salford Predictive Modeler’s CART® modeling engine is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science. CART is one of the most important tools in modern data mining.

Proprietary Code:

Technically, the CART modeling engine is based on landmark mathematical theory introduced in 1984 by four world-renowned statisticians at Stanford University and the University of California at Berkeley. The CART Modeling Engine, SPM’s implementation of Classification and Regression Trees, is the only decision tree software embodying the original proprietary code.

Fast and Versatile:

Patented extensions to the CART modeling engine are specifically designed to enhance results for market research and web analytics. The CART modeling engine supports high-speed deployment, allowing Salford Predictive Modeler’s models to predict and score in real time on a massive scale. Over the years the CART modeling engine has become known as one of the most popular and easy-to-use predictive modeling algorithms available to the analyst, it is also used as a foundation to many modern data mining approaches based on bagging and boosting.

 

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CART®

CART® - Classification and Regression Trees

CART® and Large Datasets

CART is capable of determining the number of records in your data sets, and uses this information to predict the memory and workspace requirements for trees that you build. Also, CART will read your entire data set each time a tree is built. At times these actions may be problematic, especially if you have enormous data sets.

CART® Supported File Types

CART Supported File Types

The CART® data-translation engine supports data conversions for more than 80 file formats, including popular statistical-analysis packages such as SAS® and SPSS®, databases such as Oracle and Informix, and spreadsheets such as Microsoft Excel and Lotus 1-2-3.

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How are nominal (ordered) predictors and rank related?

One of the strengths of CART® is that, for ordered predictors, the only information CART uses are the rank orders of the data – not the actual value of the data. In other words, if you replace a predictor with its rank order, the CART tree will be unchanged.

How to I define penalties to make it harder for a predictor to become the primary splitter in the node?

CART supports three "improvement penalties." The "natural" improvement for a splitter is always computed according to the CART methodology. A penalty may be imposed, however, that causes the improvement to be lessened depending, affecting the penalized splitter´s relative ranking among competitor splits. If the penalty is enough to cause the top competitor to be replaced by a competitor, the tree is changed.

Model Deployment

Any CART model can be easily deployed when translated into one of the supported languages (SAS®-compatible, C, Java, and PMML) or into the classic text output. This is critical for using your CART trees in large scale production work.

The decision logic of a CART tree, including the surrogate rules utilized if primary splitting values are missing, is automatically implemented. The resulting source code can be dropped into external applications, thus eliminating errors due to hand coding of decision rules and enabling fast and accurate model deployment.

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SPM® 8.2 Software Suite Demonstrations

Introduction to SPM® 8.2 Software & Exploring Data

 

A Fast Introduction to RandomForests® Software

 

CART® Software For Regression: Part I

 
This video provides an introduction to CART® software using the SPM® 8.2 Software Suite.

Introduction to MARS® Software for Regression

 

Introduction to TreeNet® Software for Binary Classification

 

Scoring New Data (Generate Predictions)

 
Table of Contents: click the button to the left of the full screen button (hover your mouse over the lower right hand corner of the video)

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What if there are too many levels in a categorical predictor?

CART® will only search over all possible subsets of a categorical predictor for a limited number of levels. Beyond a threshold set by computational feasibility, CART will simply reject the problem. You can control this limit with the BOPTION NCLASSES = m command, but be aware that for m larger than 15, computation times increase dramatically.

What is CART®?

CART® is an acronym for Classification and Regression Trees, a decision-tree procedure introduced in 1984 by world-renowned UC Berkeley and Stanford statisticians, Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone. Their landmark work created the modern field of sophisticated, mathematically- and theoretically-founded decision trees. The CART methodology solves a number of performance, accuracy, and operational problems that still plague many other current decision-tree methods. CART's innovations include:

What is cross validation?

Cross-validation is a method for estimating what the error rate of a sub-tree (of the maximal tree) would be if you had test data. Regardless of what value you set for V-fold cross validation, CART grows the same maximal tree. The monograph provides evidence that using a V of 10-20 gives better results than using a smaller number, but each number could result in a slightly different error estimate. The optimal tree — which is derived from the maximal tree by pruning — could differ from one V to another because each cross-validation run will come up with slightly different estimates of the error rates of sub-trees and thus might differ in which tree was actually best.

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