JMP Genomics Starter | Predictive Modeling | 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.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.

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• Can be represented by a multivariate normal distribution with known classes

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• Predictions based on the set of k training observations that are closest in feature space distance (instance-based learning)

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• More variables than observations (wide data sets)

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• The median or particular quantiles of the dependent variables are better measures of central tendency than the mean

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• More variables than observations (wide data sets)

• Dimensions of calculations are based on the number of observations, rather than the number of variables

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• More variables than observations (wide data sets)

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• Caution: This process can be computationally intensive for large data sets.

• Click to sets up a predictive modeling review that can be used to compare the efficacy of different models, applied to one or more dependent variables, at making predictions under the same conditions and compare the models using cross validation, test sets, or learning curves.See Predictive Modeling for other subcategories.