Documentation
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Overview ¶
Package tree provides decision tree algorithms.
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type DecisionTree ¶
type DecisionTree struct {
// MaxDepth is the maximum depth of the tree (0 = unlimited)
MaxDepth int
// MinSamples is the minimum samples required to split a node
MinSamples int
// Criterion for splitting: "gini", "entropy" (classification), "mse" (regression)
Criterion string
// contains filtered or unexported fields
}
DecisionTree implements a decision tree for classification and regression.
func NewDecisionTreeClassifier ¶
func NewDecisionTreeClassifier(maxDepth, minSamples int, criterion string) *DecisionTree
NewDecisionTreeClassifier creates a new decision tree for classification.
func NewDecisionTreeRegressor ¶
func NewDecisionTreeRegressor(maxDepth, minSamples int) *DecisionTree
NewDecisionTreeRegressor creates a new decision tree for regression.
func (*DecisionTree) FeatureImportances ¶
func (dt *DecisionTree) FeatureImportances() []float64
FeatureImportances returns the importance of each feature.
Source Files
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