DecisionTreeClassifier class abstract
A class that performs decision tree-based classification
Constructors
- DecisionTreeClassifier(DataFrame trainData, String targetName, {num minError = 0.5, int minSamplesCount = 1, int maxDepth = 10, DType dtype = dTypeDefaultValue, TreeAssessorType assessorType = TreeAssessorType.gini})
-
Parameters:
factory
- DecisionTreeClassifier.fromJson(String json)
-
Restores previously fitted classifier instance from the given
jsonfactory
Properties
- assessorType → TreeAssessorType
-
An assessment type that was applied to the initial data in order to
decide how to split it while building the tree
no setter
- dtype → DType
-
A type for all numeric values using by the
Predictorno setterinherited - hashCode → int
-
The hash code for this object.
no setterinherited
- maxDepth → int
-
A maximum number of decision tree levels.
no setter
- minError → num
-
A minimal error on a single decision tree node. It is used as a
stop criteria to avoid further decision tree node splitting: if the
node is good enough, there is no need to split it and thus it can be
considered a leaf.
no setter
- minSamplesCount → int
-
A minimal number of samples (observations) on the
decision's tree node. The value is used as a stop criteria to avoid
further decision tree node splitting: if the node contains less than or
equal to minSamplesCount observations, the node is considered a leaf.
no setter
- negativeLabel → num
-
A value using to encode negative class.
no setterinherited
- positiveLabel → num
-
A value using to encode positive class.
no setterinherited
- runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
- schemaVersion → int?
-
Contains a version of the current json schema
no setterinherited
-
targetNames
→ Iterable<
String> -
A collection of target column names of a dataset which was used to learn the ML
model
no setterinherited
Methods
-
assess(
DataFrame observations, MetricType metricType) → double -
Assesses model performance according to provided
metricTypeinherited -
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
predict(
DataFrame testFeatures) → DataFrame -
Returns prediction, based on the learned coefficients
inherited
-
predictProbabilities(
DataFrame testFeatures) → DataFrame -
Returns predicted distribution of probabilities for each observation in
the passed
testFeaturesinherited -
retrain(
DataFrame data) → DecisionTreeClassifier -
Re-runs the learning process on the new training
data. The features, model algorithm, and hyperparameters remain the same.inherited -
saveAsJson(
String filePath) → Future< File> -
Saves a json-serializable map into a newly created file with the path
filePathinherited -
saveAsSvg(
String filePath) → Future< File> - Saves the tree as an SVG-image. Example:
-
toJson(
) → Map< String, dynamic> -
Returns a json-serializable map
inherited
-
toString(
) → String -
A string representation of this object.
inherited
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited