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).
Classification and Regression Trees
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 Classification and Regression Trees are one of Salford Systems flagship products, and is a core component within the Salford Predictive Modeler software suite.
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.
CART System Requirements - short introduction
This guide provides a detailed description of classification modeling in CART.
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.
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.
This guide provides a brief introduction to CART.
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.
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)
You wish to apply your results to new data, but CASE will not accept the data.
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.