RTrees Properties |
The RTrees type exposes the following members.
Name | Description | |
---|---|---|
ActiveVarCount |
The size of the randomly selected subset of features at each tree node
and that are used to find the best split(s).
| |
AllocatedMemory |
Gets or sets a memory address allocated by AllocMemory.
(Inherited from DisposableObject.) | |
AllocatedMemorySize |
Gets or sets the byte length of the allocated memory
(Inherited from DisposableObject.) | |
CalculateVarImportance |
If true then variable importance will be calculated and then
it can be retrieved by RTrees::getVarImportance. Default value is false.
| |
CVFolds |
If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
cross-validation procedure where K is equal to CVFolds. Default value is 10.
(Inherited from DTrees.) | |
CvPtr |
Native pointer of OpenCV structure
(Inherited from DisposableCvObject.) | |
DataHandle |
Gets or sets a handle which allocates using cvSetData.
(Inherited from DisposableObject.) | |
Empty |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
(Inherited from Algorithm.) | |
IsDisposed |
Gets a value indicating whether this instance has been disposed.
(Inherited from DisposableObject.) | |
IsEnabledDispose |
Gets or sets a value indicating whether you permit disposing this instance.
(Inherited from DisposableObject.) | |
MaxCategories |
Cluster possible values of a categorical variable into
K < =maxCategories clusters to find a suboptimal split.
(Inherited from DTrees.) | |
MaxDepth |
The maximum possible depth of the tree.
(Inherited from DTrees.) | |
MinSampleCount |
If the number of samples in a node is less than this parameter then the
node will not be split. Default value is 10.
(Inherited from DTrees.) | |
Priors |
The array of a priori class probabilities, sorted by the class label value.
(Inherited from DTrees.) | |
RegressionAccuracy |
Termination criteria for regression trees.
If all absolute differences between an estimated value in a node and
values of train samples in this node are less than this parameter
then the node will not be split further. Default value is 0.01f.
(Inherited from DTrees.) | |
TermCriteria |
The termination criteria that specifies when the training algorithm stops.
| |
TruncatePrunedTree |
If true then pruned branches are physically removed from the tree.
Otherwise they are retained and it is possible to get results from the
original unpruned (or pruned less aggressively) tree. Default value is true.
(Inherited from DTrees.) | |
Use1SERule |
If true then a pruning will be harsher.
This will make a tree more compact and more resistant to the training
data noise but a bit less accurate. Default value is true.
(Inherited from DTrees.) | |
UseSurrogates |
If true then surrogate splits will be built.
These splits allow to work with missing data and compute variable
importance correctly. Default value is false.
(Inherited from DTrees.) |