cv.RTrees - mexopencv

Random Trees

The class implements the random forest predictor.

Random Trees

Random trees have been introduced by [BreimanCutler]. The algorithm can deal with both classification and regression problems. Random trees is a collection (ensemble) of tree predictors that is called forest further in this section (the term has been also introduced by L. Breiman). The classification works as follows: the random trees classifier takes the input feature vector, classifies it with every tree in the forest, and outputs the class label that recieved the majority of votes.In case of a regression, the classifier response is the average of the responses over all the trees in the forest.

All the trees are trained with the same parameters but on different training sets. These sets are generated from the original training set using the bootstrap procedure: for each training set, you randomly select the same number of vectors as in the original set (=N). The vectors are chosen with replacement. That is, some vectors will occur more than once and some will be absent. At each node of each trained tree, not all the variables are used to find the best split, but a random subset of them. With each node a new subset is generated. However, its size is fixed for all the nodes and all the trees. It is a training parameter set to sqrt(#variables) by default. None of the built trees are pruned.

In random trees there is no need for any accuracy estimation procedures, such as cross-validation or bootstrap, or a separate test set to get an estimate of the training error. The error is estimated internally during the training. When the training set for the current tree is drawn by sampling with replacement, some vectors are left out (so-called oob (out-of-bag) data). The size of oob data is about N/3. The classification error is estimated by using this oob-data as follows:

Get a prediction for each vector, which is oob relative to the i-th tree, using the very i-th tree.
After all the trees have been trained, for each vector that has ever been oob, find the class-winner for it (the class that has got the majority of votes in the trees where the vector was oob) and compare it to the ground-truth response.
Compute the classification error estimate as a ratio of the number of misclassified oob vectors to all the vectors in the original data. In case of regression, the oob-error is computed as the squared error for oob vectors difference divided by the total number of vectors.

References

[BreimanCutler]:

Leo Breiman and Adele Cutler: Random Forests

[1]:

Machine Learning, Wald I, July 2002. PDF

[2]:

Looking Inside the Black Box, Wald II, July 2002. PDF

[3]:

Software for the Masses, Wald III, July 2002. PDF

[4]:

And other articles from the web site

Superclasses	handle
Sealed	false
Construct on load	false

ActiveVarCount	The size of the randomly selected subset of features at each tree
CVFolds	If `CVFolds > 1` then algorithms prunes the built decision tree
CalculateVarImportance	Whether to compute variables importance.
MaxCategories	Cluster possible values of a categorical variable into
MaxDepth	The maximum possible depth of the tree.
MinSampleCount	If the number of samples in a node is less than this parameter then
Priors	The array of a priori class probabilities, sorted by the class label
RegressionAccuracy	Termination criteria for regression trees.
TermCriteria	The termination criteria that specifies when the training algorithm
TruncatePrunedTree	If true then pruned branches are physically removed from the tree.
Use1SERule	If true then a pruning will be harsher.
UseSurrogates	If true then surrogate splits will be built.
id	Object ID

	addlistener	Add listener for event.
	calcError	Computes error on the training or test dataset
	clear	Clears the algorithm state
	delete	Destructor
	empty	Returns true if the algorithm is empty
	eq	== (EQ) Test handle equality.
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	getDefaultName	Returns the algorithm string identifier
	getNodes	Returns all the nodes
	getRoots	Returns indices of root nodes
	getSplits	Returns all the splits
	getSubsets	Returns all the bitsets for categorical splits
	getVarCount	Returns the number of variables in training samples
	getVarImportance	Returns the variable importance array
	getVotes	Returns the result of each individual tree in the forest
	gt	> (GT) Greater than relation for handles.
	isClassifier	Returns true if the model is a classifier
	isTrained	Returns true if the model is trained
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	load	Loads algorithm from a file or a string
	lt	< (LT) Less than relation for handles.
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.
	predict	Predicts response(s) for the provided sample(s)
	save	Saves the algorithm parameters to a file or a string
	train	Trains the Random Trees model