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Visible to Intel only — GUID: GUID-EFB6F5C0-74D4-4521-858F-053EEF898C3A
Decision Forest Classification and Regression (DF)
Decision Forest (DF) classification and regression algorithms are based on an ensemble of tree-structured classifiers, which are known as decision trees. Decision forest is built using the general technique of bagging, a bootstrap aggregation, and a random choice of features. For more details, see [Breiman84] and [Breiman2001].
Operation |
Computational methods |
Programming Interface |
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Mathematical formulation
Refer to Developer Guide: Decision Forest Classification and Regression.
Programming Interface
All types and functions in this section are declared in the oneapi::dal::decision_forest namespace and are available via inclusion of the oneapi/dal/algo/decision_forest.hpp header file.
Enum classes
error_metric_mode
- error_metric_mode::none
-
Do not compute error metric.
- error_metric_mode::out_of_bag_error
-
Train produces table with cumulative prediction error for out of bag observations.
- error_metric_mode::out_of_bag_error_per_observation
-
Train produces table with prediction error for out-of-bag observations.
variable_importance_mode
- variable_importance_mode::none
-
Do not compute variable importance.
- variable_importance_mode::mdi
-
Mean Decrease Impurity. Computed as the sum of weighted impurity decreases for all nodes where the variable is used, averaged over all trees in the forest.
- variable_importance_mode::mda_raw
-
Mean Decrease Accuracy (permutation importance). For each tree, the prediction error on the out-of-bag portion of the data is computed (error rate for classification, MSE for regression). The same is done after permuting each predictor variable. The difference between the two are then averaged over all trees.
- variable_importance_mode::mda_scaled
-
Mean Decrease Accuracy (permutation importance). This is MDA_Raw value scaled by its standard deviation.
infer_mode
- infer_mode::class_labels
-
Infer produces a “math:n times 1 table with the predicted labels.
- infer_mode::class_responses
-
deprecated
- infer_mode::class_probabilities
-
Infer produces table with the predicted class probabilities for each observation.
voting_mode
- voting_mode::weighted
-
The final prediction is combined through a weighted majority voting.
- voting_mode::unweighted
-
The final prediction is combined through a simple majority voting.
Descriptor
template<typenameFloat=float,typenameMethod=method::by_default,typenameTask=task::by_default>classdescriptor
- Template Parameters
-
Float – The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
Method – Tag-type that specifies an implementation of algorithm. Can be method::dense or method::hist.
Task – Tag-type that specifies type of the problem to solve. Can be task::classification or task::regression.
Constructors
descriptor()=default
Creates a new instance of the class with the default property values.
Properties
error_metric_modeerror_metric_mode
The error metric mode. Default value: error_metric_mode::none.
- Getter & Setter
-
error_metric_mode get_error_metric_mode() const
auto & set_error_metric_mode(error_metric_mode value)
std::int64_tmax_bins
The maximal number of discrete bins to bucket continuous features. Used with method::hist split-finding method only. Increasing the number results in higher computation costs. Default value: 256.
- Getter & Setter
-
std::int64_t get_max_bins() const
auto & set_max_bins(std::int64_t value)
- Invariants
-
max_bins > 1
std::int64_tmax_tree_depth
The maximal depth of the tree. If 0, then nodes are expanded until all leaves are pure or until all leaves contain less or equal to min observations in leaf node samples. Default value: 0.
- Getter & Setter
-
std::int64_t get_max_tree_depth() const
auto & set_max_tree_depth(std::int64_t value)
std::int64_tseed
Seed for the random numbers generator used by the algorithm.
- Getter & Setter
-
std::int64_t get_seed() const
auto & set_seed(std::int64_t value)
- Invariants
-
tree_count > 0
doubleimpurity_threshold
The impurity threshold, a node will be split if this split induces a decrease of the impurity greater than or equal to the input value. Default value: 0.0.
- Getter & Setter
-
double get_impurity_threshold() const
auto & set_impurity_threshold(double value)
- Invariants
-
impurity_threshold >= 0.0
variable_importance_modevariable_importance_mode
The variable importance mode. Default value: variable_importance_mode::none.
- Getter & Setter
-
variable_importance_mode get_variable_importance_mode() const
auto & set_variable_importance_mode(variable_importance_mode value)
boolbootstrap
The bootstrap mode, if true, the training set for a tree is a bootstrap of the whole training set, if False, the whole dataset is used to build each tree. Default value: true.
- Getter & Setter
-
bool get_bootstrap() const
auto & set_bootstrap(bool value)
std::int64_tmin_bin_size
The minimal number of observations in a bin. Used with method::hist split-finding method only. Default value: 5.
- Getter & Setter
-
std::int64_t get_min_bin_size() const
auto & set_min_bin_size(std::int64_t value)
- Invariants
-
min_bin_size > 0
std::int64_ttree_count
The number of trees in the forest. Default value: 100.
- Getter & Setter
-
std::int64_t get_tree_count() const
auto & set_tree_count(std::int64_t value)
- Invariants
-
tree_count > 0
doublemin_impurity_decrease_in_split_node
The min impurity decrease in a split node is a threshold for stopping the tree growth early. A node will be split if its impurity is above the threshold, otherwise it is a leaf. Default value: 0.0.
- Getter & Setter
-
double get_min_impurity_decrease_in_split_node() const
auto & set_min_impurity_decrease_in_split_node(double value)
- Invariants
std::int64_tmin_observations_in_leaf_node
The minimal number of observations in a leaf node. Default value: 1 for classification, 5 for regression.
- Getter & Setter
-
std::int64_t get_min_observations_in_leaf_node() const
auto & set_min_observations_in_leaf_node(std::int64_t value)
- Invariants
voting_modevoting_mode
The voting mode. Used with task::classification only.
- Getter & Setter
-
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> voting_mode get_voting_mode() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_voting_mode(voting_mode value)
doubleobservations_per_tree_fraction
The fraction of observations per tree. Default value: 1.0.
- Getter & Setter
-
double get_observations_per_tree_fraction() const
auto & set_observations_per_tree_fraction(double value)
- Invariants
-
observations_per_tree_fraction > 0.0
observations_per_tree_fraction <= 1.0
infer_modeinfer_mode
The infer mode. Used with task::classification only.
- Getter & Setter
-
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> infer_mode get_infer_mode() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_infer_mode(infer_mode value)
std::int64_tmin_observations_in_split_node
The minimal number of observations in a split node. Default value: 2.
- Getter & Setter
-
std::int64_t get_min_observations_in_split_node() const
auto & set_min_observations_in_split_node(std::int64_t value)
- Invariants
std::int64_tclass_count
The class count. Used with task::classification only. Default value: 2.
- Getter & Setter
-
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> std::int64_t get_class_count() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_class_count(std::int64_t value)
boolmemory_saving_mode
The memory saving mode. Default value: false.
- Getter & Setter
-
bool get_memory_saving_mode() const
auto & set_memory_saving_mode(bool value)
std::int64_tfeatures_per_node
The number of features to consider when looking for the best split for a node. Default value: task::classification ? sqrt(p) : p/3, where p is the total number of features.
- Getter & Setter
-
std::int64_t get_features_per_node() const
auto & set_features_per_node(std::int64_t value)
doublemin_weight_fraction_in_leaf_node
The min weight fraction in a leaf node. The minimum weighted fraction of the total sum of weights (of all input observations) required to be at a leaf node. Default value: 0.0.
- Getter & Setter
-
double get_min_weight_fraction_in_leaf_node() const
auto & set_min_weight_fraction_in_leaf_node(double value)
- Invariants
-
min_weight_fraction_in_leaf_node >= 0.0
min_weight_fraction_in_leaf_node <= 0.5
std::int64_tmax_leaf_nodes
The maximal number of the leaf nodes. If 0, the number of leaf nodes is not limited. Default value: 0.
- Getter & Setter
-
std::int64_t get_max_leaf_nodes() const
auto & set_max_leaf_nodes(std::int64_t value)
Method tags
structdense
Tag-type that denotes dense computational method.
structhist
Tag-type that denotes hist computational method.
usingby_default=dense
Alias tag-type for dense computational method.
Task tags
structclassification
Tag-type that parameterizes entities used for solving classification problem.
structregression
Tag-type that parameterizes entities used for solving regression problem.
usingby_default=classification
Alias tag-type for classification task.
Model
template<typenameTask=task::by_default>classmodel
- Template Parameters
-
Task – Tag-type that specifies the type of the problem to solve. Can be task::classification or task::regression.
Constructors
model()
Creates a new instance of the class with the default property values.
Public Methods
std::int64_tget_tree_count()const
The number of trees in the forest.
template<typenameT=Task,typenameNone=detail::enable_if_classification_t<T>>std::int64_tget_class_count()const
The class count. Used with oneapi::dal::decision_forest::task::classification only.
template<typenameVisitor>voidtraverse_depth_first(std::int64_ttree_idx, Visitor&&visitor)const
Performs Depth First Traversal of i-th tree.
- Parameters
-
tree_idx – Index of the tree to traverse.
visitor – This functor gets notified when tree nodes are visited, via corresponding operators: bool operator()(const decision_forest::split_node_info<Task>&) bool operator()(const decision_forest::leaf_node_info<Task>&).
template<typenameVisitor>voidtraverse_breadth_first(std::int64_ttree_idx, Visitor&&visitor)const
Performs Breadth First Traversal of i-th tree.
- Parameters
-
tree_idx – Index of the tree to traverse.
visitor – This functor gets notified when tree nodes are visited, via corresponding operators: bool operator()(const decision_forest::split_node_info<Task>&) bool operator()(const decision_forest::leaf_node_info<Task>&).
Training train(...)
Input
template<typenameTask=task::by_default>classtrain_input
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::classification or task::regression.
Constructors
train_input(consttable&data, consttable&responses, consttable&weights=table{})
Creates a new instance of the class with the given data, responses and weights property values.
Properties
consttable&data
The training set . Default value: table{}.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &value)
consttable&weights
The vector of weights for the training set . Default value: table{}.
- Getter & Setter
-
const table & get_weights() const
auto & set_weights(const table &value)
consttable&responses
Vector of responses for the training set . Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
auto & set_responses(const table &value)
consttable&labels
Vector of labels for the training set . Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
auto & set_labels(const table &value)
Result
template<typenameTask=task::by_default>classtrain_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::classification or task::regression.
Constructors
train_result()
Creates a new instance of the class with the default property values.
Properties
consttable&oob_err
A table containing cumulative out-of-bag error value. Computed when error_metric_mode set with error_metric_mode::out_of_bag_error. Default value: table{}.
- Getter & Setter
-
const table & get_oob_err() const
auto & set_oob_err(const table &value)
constmodel<Task>&model
The trained Decision Forest model. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
consttable&var_importance
A table containing variable importance value for each feature. Computed when variable_importance_mode!=variable_importance_mode::none. Default value: table{}.
- Getter & Setter
-
const table & get_var_importance() const
auto & set_var_importance(const table &value)
consttable&oob_err_per_observation
A table containing out-of-bag error value per observation. Computed when error_metric_mode set with error_metric_mode::out_of_bag_error_per_observation. Default value: table{}.
- Getter & Setter
-
const table & get_oob_err_per_observation() const
auto & set_oob_err_per_observation(const table &value)
Operation
template<typenameDescriptor>decision_forest::train_resulttrain(constDescriptor&desc, constdecision_forest::train_input&input)
- Parameters
-
desc – Decision Forest algorithm descriptor decision_forest::descriptor.
input – Input data for the training operation
- Preconditions
-
input.data.is_empty == false
input.labels.is_empty == false
input.labels.column_count == 1
input.data.row_count == input.labels.row_count
desc.get_bootstrap() == true || (desc.get_bootstrap() == false && desc.get_variable_importance_mode() != variable_importance_mode::mda_raw && desc.get_variable_importance_mode() != variable_importance_mode::mda_scaled)
desc.get_bootstrap() == true || (desc.get_bootstrap() == false && desc.get_error_metric_mode() == error_metric_mode::none)
Inference infer(...)
Input
template<typenameTask=task::by_default>classinfer_input
- Template Parameters
-
Task – Tag-type that specifies the type of the problem to solve. Can be task::classification or task::regression.
Constructors
infer_input(constmodel<Task>&trained_model, consttable&data)
Creates a new instance of the class with the given model and data property values.
Properties
consttable&data
The dataset for inference . Default value: table{}.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &value)
constmodel<Task>&model
The trained Decision Forest model. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
Result
template<typenameTask=task::by_default>classinfer_result
- Template Parameters
-
Task – Tag-type that specifies the type of the problem to solve. Can be task::classification or task::regression.
Constructors
infer_result()
Creates a new instance of the class with the default property values.
Properties
consttable&labels
The table with the predicted labels. Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
auto & set_labels(const table &value)
consttable&probabilities
A table with the predicted class probabilities for each observation.
- Getter & Setter
-
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> const table & get_probabilities() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_probabilities(const table &value)
consttable&responses
The table with the predicted responses. Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
auto & set_responses(const table &value)
Operation
template<typenameDescriptor>decision_forest::infer_resultinfer(constDescriptor&desc, constdecision_forest::infer_input&input)
- Parameters
-
desc – Decision Forest algorithm descriptor decision_forest::descriptor.
input – Input data for the inference operation
- Preconditions
-
input.data.is_empty == false