Package com.imsl.test.example.datamining

Data mining examples.

See: Description

Class Summary

Class	Description
AprioriEx1	Finds frequent itemsets and strong association rules for a small set of transactions.
AprioriEx2	Applies the Apriori algorithm to separate sets of transactions.
BootstrapAggregationEx1	Performs bootstrap aggregation on a decision tree.
BootstrapAggregationEx2	Performs bootstrap aggregation on a logistic regression model.
CrossValidationEx1	Uses cross-validation to determine the optimally pruned decision tree.
GradientBoostingEx1	Predicts a regression response variable based on 6 predictor variables.
GradientBoostingEx2	Predicts a binary response variable based on 4 predictor variables.
GradientBoostingEx3	Selects the number of iterations using cross-validation.
GradientBoostingEx4	Uses an input model to set the configuration of the base learner.
GradientBoostingModelObjectEx1	Uses a trained gradient boosting model to predict a new data set.
GradientBoostingModelObjectEx2	Reads in a trained gradient boosting model object to predict a new data set.
KohonenSOMEx1	Creates and trains a Kohonen self-organizing map.
LogisticRegressionEx1	Trains a logistic regression model for a binomial response variable.
LogisticRegressionEx2	Trains a logistic regression model for a multinomial response.
LogisticRegressionEx3	Trains a logistic regression model for multinomial count data.
LogisticRegressionModelObjectEx1	Uses a trained logistic regression model to predict new data.
LogisticRegressionModelObjectEx2	Aggregates two separate fits of logistic regression.
NaiveBayesClassifierEx1	Trains a classifier to Fisher's Iris data.
NaiveBayesClassifierEx2	Trains a classifier on nominal (categorical) attributes.
NaiveBayesClassifierEx3	Trains a classifier with a user supplied probability function.
NaiveBayesClassifierEx3.TestGaussFcn1	Defines the user supplied probability distribution.

Package com.imsl.test.example.datamining Description

Data mining examples.