The ML classes discussed in this section implement Classification and Regression Tree algorithms described in [Breiman84].
The class
CvDTree
represents a single decision tree that may be used alone or as a base class in tree ensembles (see
Boosting and
Random Trees ).
A decision tree is a binary tree (tree where each nonleaf node has two child nodes). It can be used either for classification or for regression. For classification, each tree leaf is marked with a class label; multiple leaves may have the same label. For regression, a constant is also assigned to each tree leaf, so the approximation function is piecewise constant.
To reach a leaf node and to obtain a response for the input feature vector, the prediction procedure starts with the root node. From each nonleaf node the procedure goes to the left (selects the left child node as the next observed node) or to the right based on the value of a certain variable whose index is stored in the observed node. The following variables are possible:
So, in each node, a pair of entities (variable_index
, decision_rule
(threshold/subset)
) is used. This pair is called a split (split on
the variable variable_index
). Once a leaf node is reached, the value
assigned to this node is used as the output of the prediction procedure.
Sometimes, certain features of the input vector are missed (for example, in the darkness it is difficult to determine the object color), and the prediction procedure may get stuck in the certain node (in the mentioned example, if the node is split by color). To avoid such situations, decision trees use socalled surrogate splits. That is, in addition to the best “primary” split, every tree node may also be split to one or more other variables with nearly the same results.
The tree is built recursively, starting from the root node. All training data (feature vectors and responses) is used to split the root node. In each node the optimum decision rule (the best “primary” split) is found based on some criteria. In machine learning, gini
“purity” criteria are used for classification, and sum of squared errors is used for regression. Then, if necessary, the surrogate splits are found. They resemble the results of the primary split on the training data. All the data is divided using the primary and the surrogate splits (like it is done in the prediction procedure) between the left and the right child node. Then, the procedure recursively splits both left and right nodes. At each node the recursive procedure may stop (that is, stop splitting the node further) in one of the following cases:
When the tree is built, it may be pruned using a crossvalidation procedure, if necessary. That is, some branches of the tree that may lead to the model overfitting are cut off. Normally, this procedure is only applied to standalone decision trees. Usually tree ensembles build trees that are small enough and use their own protection schemes against overfitting.
Besides the prediction that is an obvious use of decision trees, the tree can be also used for various data analyses. One of the key properties of the constructed decision tree algorithms is an ability to compute the importance (relative decisive power) of each variable. For example, in a spam filter that uses a set of words occurred in the message as a feature vector, the variable importance rating can be used to determine the most “spamindicating” words and thus help keep the dictionary size reasonable.
Importance of each variable is computed over all the splits on this variable in the tree, primary and surrogate ones. Thus, to compute variable importance correctly, the surrogate splits must be enabled in the training parameters, even if there is no missing data.
CvDTreeSplit
¶The structure represents a possible decision tree node split. It has public members:
var_idx
¶Index of variable on which the split is created.
inversed
¶If it is not null then inverse split rule is used that is left and right branches are exchanged in the rule expressions below.
quality
¶The split quality, a positive number. It is used to choose the best primary split, then to choose and sort the surrogate splits. After the tree is constructed, it is also used to compute variable importance.
next
¶Pointer to the next split in the node list of splits.
subset
¶Bit array indicating the value subset in case of split on a categorical variable. The rule is:
if var_value in subset
then next_node < left
else next_node < right
ord::
c
¶The threshold value in case of split on an ordered variable. The rule is:
if var_value < ord.c
then next_node<left
else next_node<right
ord::
split_point
¶Used internally by the training algorithm.
CvDTreeNode
¶The structure represents a node in a decision tree. It has public members:
class_idx
¶Class index normalized to 0..class_count1 range and assigned to the node. It is used internally in classification trees and tree ensembles.
Tn
¶Tree index in a ordered sequence of pruned trees. The indices are used during and after the pruning procedure. The root node has the maximum value Tn
of the whole tree, child nodes have Tn
less than or equal to the parent’s Tn
, and nodes with are not used at prediction stage (the corresponding branches are considered as cutoff), even if they have not been physically deleted from the tree at the pruning stage.
value
¶Value at the node: a class label in case of classification or estimated function value in case of regression.
parent
¶Pointer to the parent node.
left
¶Pointer to the left child node.
right
¶Pointer to the right child node.
split
¶Pointer to the first (primary) split in the node list of splits.
sample_count
¶The number of samples that fall into the node at the training stage. It is used to resolve the difficult cases  when the variable for the primary split is missing and all the variables for other surrogate splits are missing too. In this case the sample is directed to the left if left>sample_count > right>sample_count
and to the right otherwise.
depth
¶Depth of the node. The root node depth is 0, the child nodes depth is the parent’s depth + 1.
Other numerous fields of CvDTreeNode
are used internally at the training stage.
CvDTreeParams
¶The structure contains all the decision tree training parameters. You can initialize it by default constructor and then override any parameters directly before training, or the structure may be fully initialized using the advanced variant of the constructor.
The constructors.
CvDTreeParams::
CvDTreeParams
()¶
CvDTreeParams::
CvDTreeParams
(int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, int cv_folds, bool use_1se_rule, bool truncate_pruned_tree, const float* priors)¶Parameters: 


The default constructor initializes all the parameters with the default values tuned for the standalone classification tree:
CvDTreeParams() : max_categories(10), max_depth(INT_MAX), min_sample_count(10),
cv_folds(10), use_surrogates(true), use_1se_rule(true),
truncate_pruned_tree(true), regression_accuracy(0.01f), priors(0)
{}
CvDTreeTrainData
¶Decision tree training data and shared data for tree ensembles. The structure is mostly used internally for storing both standalone trees and tree ensembles efficiently. Basically, it contains the following types of information:
CvDTreeParams
.There are two ways of using this structure. In simple cases (for example, a standalone tree or the readytouse “black box” tree ensemble from machine learning, like
Random Trees or
Boosting ), there is no need to care or even to know about the structure. You just construct the needed statistical model, train it, and use it. The CvDTreeTrainData
structure is constructed and used internally. However, for custom tree algorithms or another sophisticated cases, the structure may be constructed and used explicitly. The scheme is the following:
set_data
, or it is built using the full form of constructor. The parameter _shared
must be set to true
.CvDTree::train()
).CvDTree
: public CvStatModel
¶The class implements a decision tree as described in the beginning of this section.
Trains a decision tree.
bool CvDTree::
train
(const Mat& trainData, int tflag, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), const Mat& varType=Mat(), const Mat& missingDataMask=Mat(), CvDTreeParams params=CvDTreeParams() )¶
bool CvDTree::
train
(const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvDTreeParams params=CvDTreeParams() )¶
bool CvDTree::
train
(CvMLData* trainData, CvDTreeParams params=CvDTreeParams() )¶
bool CvDTree::
train
(CvDTreeTrainData* trainData, const CvMat* subsampleIdx)¶
cv2.DTree.
train
(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) → retval¶There are four train
methods in CvDTree
:
CvStatModel::train()
conventions. It is the most complete form. Both data layouts (tflag=CV_ROW_SAMPLE
and tflag=CV_COL_SAMPLE
) are supported, as well as sample and variable subsets, missing measurements, arbitrary combinations of input and output variable types, and so on. The last parameter contains all of the necessary training parameters (see the CvDTreeParams
description).CvMLData
to pass training data to a decision tree.train
is mostly used for building tree ensembles. It takes the preconstructed CvDTreeTrainData
instance and an optional subset of the training set. The indices in subsampleIdx
are counted relatively to the _sample_idx
, passed to the CvDTreeTrainData
constructor. For example, if _sample_idx=[1, 5, 7, 100]
, then subsampleIdx=[0,3]
means that the samples [1, 100]
of the original training set are used.The function is parallelized with the TBB library.
Returns the leaf node of a decision tree corresponding to the input vector.
CvDTreeNode* CvDTree::
predict
(const Mat& sample, const Mat& missingDataMask=Mat(), bool preprocessedInput=false ) const
¶
CvDTreeNode* CvDTree::
predict
(const CvMat* sample, const CvMat* missingDataMask=0, bool preprocessedInput=false ) const
¶
cv2.DTree.
predict
(sample[, missingDataMask[, preprocessedInput]]) → retval¶Parameters: 


The method traverses the decision tree and returns the reached leaf node as output. The prediction result, either the class label or the estimated function value, may be retrieved as the value
field of the CvDTreeNode
structure, for example: dtree>predict(sample,mask)>value
.
Returns error of the decision tree.
float CvDTree::
calc_error
(CvMLData* trainData, int type, std::vector<float>* resp=0 )¶Parameters: 


The method calculates error of the decision tree. In case of classification it is the percentage of incorrectly classified samples and in case of regression it is the mean of squared errors on samples.
Returns the variable importance array.
Mat CvDTree::
getVarImportance
()¶
const CvMat* CvDTree::
get_var_importance
()¶
cv2.DTree.
getVarImportance
() → retval¶Returns the root of the decision tree.
const CvDTreeNode* CvDTree::
get_root
() const
¶Returns the CvDTree::pruned_tree_idx
parameter.
int CvDTree::
get_pruned_tree_idx
() const
¶The parameter DTree::pruned_tree_idx
is used to prune a decision tree. See the CvDTreeNode::Tn
parameter.
Returns used train data of the decision tree.
CvDTreeTrainData* CvDTree::
get_data
() const
¶Example: building a tree for classifying mushrooms. See the mushroom.cpp
sample that demonstrates how to build and use the
decision tree.
[Breiman84]  Breiman, L., Friedman, J. Olshen, R. and Stone, C. (1984), Classification and Regression Trees, Wadsworth. 