| AttributeTracker.h [code] | Keep a record of which attributes are active |
| batchtest | Performs cross validation of a collection of learners on a collection of datasets |
| BeliefNet.h [code] | A Belief Net Structure with CPT local models |
| beliefnetcorrupt | Makes some random changes to a BeliefNet |
| beliefnetdata | Creates a data set by sampling from a Bayesian Network |
| beliefnetscore | Tests a BeliefNet in several ways |
| bindata | Converts continuous attributes into discrete ones |
| bitfield.h [code] | Compactly represent a bit field |
| bnlearn | Learns the structure of a BeliefNet from a data set. Designed to be easily modified |
| bnlearn-engine.h [code] | Learn the structure of a BeliefNet from data |
| C45interface.h [code] | Calls the C4.5 decision tree learning system and returns the learned tree |
| c45wrapper | Calls C4.5 and tests the learned tree |
| c50wrapper | Calls C5.0 and tests the learned tree |
| cleandata | Cleans up a data set in several ways |
| clusterdata | Creates a synthetic data set from randomly generated clusters |
| combinedata | Combines a series of data sets into a single large one |
| cvfdt | Learns a DecisionTree from a high-speed time-changing data stream (or very large data set) |
| Debug.h [code] | A set of functions that help your programs produce debugging output in a consistent way |
| decisionstump | Learns a decision stump (a DecisionTree with only one split) |
| DecisionTree.h [code] | A Decision Tree Structure |
| doxygen.h [code] | Used to hold doxygen documentation. Ignore this file |
| Example.h [code] | ADT for training (and testing, etc.) data |
| ExampleGenerator.h [code] | Generate a random (but reproducible) data set |
| ExampleGroupStats.h [code] | Sufficient statistics for Entropy and Gini |
| ExampleSpec.h [code] | Schema for training data |
| folddata | Randomly splits a data set into a collection of train/test pairs |
| HashTable.h [code] | A hash table |
| kmeans | Performs k-means clustering |
| lists.h [code] | Generic list functions |
| memory.h [code] | Tracks the size of allocations made |
| mostcommonclass | Predicts the most common class in the training data |
| naivebayes | A Naive Bayes learner |
| random.h [code] | Generates random numbers in a number of ways, and has support for saving and restoring the state of the random number generator |
| REPrune.h [code] | Peforms reduced error pruning on a decision tree |
| sampledata | Draws a sample from a data set |
| shuffledata | Randomizes the order of a data set and rewrites it |
| stats.h [code] | Some statistical functions |
| treedata | Creates a synthetic data set by sampling from a randomly generated DecisionTree |
| uRunner | Distribute a collection of jobs across a cluster of computers |
| vfbn1 | Learns the structure of a BeliefNet from a very large data set using sampling |
| vfbn2 | Learns the structure of a BeliefNet from a very large data set using sampling and a new search proceedure |
| vfdt | Learns a decision tree from a high-speed data stream or very large data set |
| vfdt-engine.h [code] | An API which lets your program learn a DecisionTree from a high-speed data stream |
| vfem | Performs EM clustering |
| vfkm | Performs k-means clustering accelerated with sampling |
| xvalidate | Performs cross validation of a learner on a data set |
