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
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 • Nominal
 • Binary
 • Ordinal
 • Can be represented by a multivariate normal distribution with known classes
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 • Based on Fisher discriminant analysis
 • Nominal
 • Binary
 • Ordinal
 • Continuous
 • Nonparametric discriminant method
Tip: Diagonal Linear Discriminant Analysis can be performed via the Euclidean Distance Metric.
 • Binary
 • Ordinal
 • Continuous
 • Flexible
 • Many model selection methods available
 • Many inclusion and stopping criteria available
 • Nominal
 • Binary
 • Ordinal
 • Fewer variables than observations
 • Nonparametric discriminant method
 • Predictions based on the set of k training observations that are closest in feature space distance (instance-based learning)
 • Nominal
 • Binary
 • Ordinal
 • Fewer variables than observations
 • Data fit to a logistic curve using a logit link function
Caution: This process can take a long time to run, depending on the number of predictor variables and the speed of your machine.
 • Binary
 • Ordinal
 • Continuous
 • More variables than observations (wide data sets)
 • Multicollinearity among predictor variables exists
 • Linear regression model
 • Simultaneously models variability in both dependent and predictor variables
 • Nominal
 • Binary
 • Ordinal
 • Continuous
 • Can be represented as a hierarchy of partitions
 • Simple tree-based rule sets from optimal splitting relationships between dependent and predictor variables are used
 • Binary
 • Ordinal
 • Continuous
 • The median or particular quantiles of the dependent variable s are better measures of central tendency than the mean
 • Flexible
 • Many model selection methods available
 • Many inclusion and stopping criteria available
 • Model robustness; data robustness to outliers
 • Binary
 • Ordinal
 • Continuous
 • More variables than observations (wide data sets)
 • Dimensions of calculations are based on the number of observations, rather than the number of variables
 • Binary
 • Ordinal
 • Continuous
 • More variables than observations (wide data sets)
 • Multicollinearity among predictor variables exists
 • Continuous dependent variable
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 • Shrinks (regresses) estimates toward a common mean
 • Binary
 • Ordinal
 • Continuous
 • Survival data with time-to-event variable (censor variable optional)
 • Fewer variables than observations
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 • Many model selection methods available
Caution: This process can be computationally intensive for large data sets.
See Predictive Modeling for other subcategories.