JMP Clinical Starter | Predictive Modeling | Main Methods

Main Methods
Click on a button corresponding to a predictive modeling main method. All processes require a wide data set. (See Tall and Wide Data Sets.) For a more thorough introduction to predictive modeling and these processes, see Predictive Modeling.
Refer to the table below for key features and general guidance on these processes. You are encouraged to explore multiple processes and use the individual process links for a more detailed explanation of each.
Tip: When in doubt, there is no harm in trying several predictive modeling methods on your data. The Predictive Modeling Review enables you to standardize model parameters and specifications. Additional tools are also available in the Model Comparisons submenu for this purpose.
Classification boundary shape for binary dependent variable
Tip: Diagonal Linear Discriminant Analysis can be performed via the Euclidean Distance Metric.
Predictions based on the set of k training observations that are closest in feature space distance (instance-based learning)
Caution: This process can take a long time to run, depending on the number of predictor variables and the speed of your machine.
Simple tree-based rule sets from optimal splitting relationships between dependent and predictor variables are used
The median or particular quantiles of the dependent variable s are better measures of central tendency than the mean
Dimensions of calculations are based on the number of observations, rather than the number of variables
Survival data with time-to-event variable (censor variable optional)
Caution: This process can be computationally intensive for large data sets.
See Predictive Modeling for other subcategories.