Documentation
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Index ¶
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type DecisionTreeNode ¶
type DecisionTreeNode struct {
Type NodeType
Children map[string]*DecisionTreeNode
SplitAttr base.Attribute
ClassDist map[string]int
Class string
ClassAttr *base.Attribute
}
DecisionTreeNode represents a given portion of a decision tree
func InferID3Tree ¶
func InferID3Tree(from *base.Instances, with RuleGenerator) *DecisionTreeNode
InferID3Tree builds a decision tree using a RuleGenerator from a set of Instances (implements the ID3 algorithm)
func (*DecisionTreeNode) Predict ¶
func (d *DecisionTreeNode) Predict(what *base.Instances) *base.Instances
Predict outputs a base.Instances containing predictions from this tree
func (*DecisionTreeNode) Prune ¶
func (d *DecisionTreeNode) Prune(using *base.Instances)
Prune eliminates branches which hurt accuracy
func (*DecisionTreeNode) String ¶
func (d *DecisionTreeNode) String() string
String returns a human-readable representation of a given node and it's children
type ID3DecisionTree ¶
type ID3DecisionTree struct {
base.BaseClassifier
Root *DecisionTreeNode
PruneSplit float64
}
ID3DecisionTree represents an ID3-based decision tree using the Information Gain metric to select which attributes to split on at each node.
func NewID3DecisionTree ¶
func NewID3DecisionTree(prune float64) *ID3DecisionTree
Returns a new ID3DecisionTree with the specified test-prune ratio. Of the ratio is less than 0.001, the tree isn't pruned
func (*ID3DecisionTree) Fit ¶
func (t *ID3DecisionTree) Fit(on *base.Instances)
Fit builds the ID3 decision tree
func (*ID3DecisionTree) Predict ¶
func (t *ID3DecisionTree) Predict(what *base.Instances) *base.Instances
Predict outputs predictions from the ID3 decision tree
func (*ID3DecisionTree) String ¶
func (t *ID3DecisionTree) String() string
String returns a human-readable version of this ID3 tree
type InformationGainRuleGenerator ¶
type InformationGainRuleGenerator struct {
}
func (*InformationGainRuleGenerator) GenerateSplitAttribute ¶
func (r *InformationGainRuleGenerator) GenerateSplitAttribute(f *base.Instances) base.Attribute
GetSplitAttribute returns the non-class Attribute which maximises the information gain.
IMPORTANT: passing a base.Instances with no Attributes other than the class variable will panic()
func (*InformationGainRuleGenerator) GetSplitAttributeFromSelection ¶
func (r *InformationGainRuleGenerator) GetSplitAttributeFromSelection(consideredAttributes []int, f *base.Instances) base.Attribute
GetSplitAttribute from selection returns the class Attribute which maximises the information gain amongst consideredAttributes
IMPORTANT: passing a zero-length consideredAttributes parameter will panic()
type RandomTree ¶
type RandomTree struct {
base.BaseClassifier
Root *DecisionTreeNode
Rule *RandomTreeRuleGenerator
}
RandomTree builds a decision tree by considering a fixed number of randomly-chosen attributes at each node
func NewRandomTree ¶
func NewRandomTree(attrs int) *RandomTree
NewRandomTree returns a new RandomTree which considers attrs randomly chosen attributes at each node.
func (*RandomTree) Fit ¶
func (rt *RandomTree) Fit(from *base.Instances)
Train builds a RandomTree suitable for prediction
func (*RandomTree) Predict ¶
func (rt *RandomTree) Predict(from *base.Instances) *base.Instances
Predict returns a set of Instances containing predictions
func (*RandomTree) Prune ¶
func (rt *RandomTree) Prune(with *base.Instances)
Prune removes nodes from the tree which are detrimental to determining the accuracy of the test set (with)
func (*RandomTree) String ¶
func (rt *RandomTree) String() string
String returns a human-readable representation of this structure
type RandomTreeRuleGenerator ¶
type RandomTreeRuleGenerator struct {
Attributes int
// contains filtered or unexported fields
}
RandomTreeRuleGenerator is used to generate decision rules for Random Trees
func (*RandomTreeRuleGenerator) GenerateSplitAttribute ¶
func (r *RandomTreeRuleGenerator) GenerateSplitAttribute(f *base.Instances) base.Attribute
GenerateSplitAttribute returns the best attribute out of those randomly chosen which maximises Information Gain
Source Files