public class DecisionTreeClassificationModel extends ProbabilisticClassificationModel<Vector,DecisionTreeClassificationModel> implements DecisionTreeModel, DecisionTreeClassifierParams, MLWritable, scala.Serializable
| Modifier and Type | Method and Description |
|---|---|
BooleanParam |
cacheNodeIds()
If false, the algorithm will pass trees to executors to match instances with nodes.
|
IntParam |
checkpointInterval()
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
|
DecisionTreeClassificationModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
int |
depth()
Depth of the tree.
|
Vector |
featureImportances() |
Param<String> |
impurity()
Criterion used for information gain calculation (case-insensitive).
|
Param<String> |
leafCol()
Leaf indices column name.
|
static DecisionTreeClassificationModel |
load(String path) |
IntParam |
maxBins()
Maximum number of bins used for discretizing continuous features and for choosing how to split
on features at each node.
|
IntParam |
maxDepth()
Maximum depth of the tree (nonnegative).
|
IntParam |
maxMemoryInMB()
Maximum memory in MB allocated to histogram aggregation.
|
DoubleParam |
minInfoGain()
Minimum information gain for a split to be considered at a tree node.
|
IntParam |
minInstancesPerNode()
Minimum number of instances each child must have after split.
|
DoubleParam |
minWeightFractionPerNode()
Minimum fraction of the weighted sample count that each child must have after split.
|
int |
numClasses()
Number of classes (values which the label can take).
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
double |
predict(Vector features)
Predict label for the given features.
|
Vector |
predictRaw(Vector features)
Raw prediction for each possible label.
|
static MLReader<DecisionTreeClassificationModel> |
read() |
Node |
rootNode()
Root of the decision tree
|
LongParam |
seed()
Param for random seed.
|
String |
toString()
Summary of the model
|
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector. |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, predictProbability, probabilityCol, setProbabilityCol, setThresholds, thresholdsrawPredictionCol, setRawPredictionCol, transformImplfeaturesCol, labelCol, predictionCol, setFeaturesCol, setPredictionColtransform, transform, transformparamsgetLeafField, leafIterator, maxSplitFeatureIndex, numNodes, predictLeaf, toDebugStringvalidateAndTransformSchemagetCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, setLeafColgetCheckpointIntervalgetWeightColgetImpurity, getOldImpurityextractInstancesextractInstances, extractInstancesgetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwngetRawPredictionCol, rawPredictionColgetProbabilityCol, probabilityColgetThresholds, thresholdssave$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic static MLReader<DecisionTreeClassificationModel> read()
public static DecisionTreeClassificationModel load(String path)
public final Param<String> impurity()
TreeClassifierParamsimpurity in interface TreeClassifierParamspublic final Param<String> leafCol()
DecisionTreeParamsleafCol in interface DecisionTreeParamspublic final IntParam maxDepth()
DecisionTreeParamsmaxDepth in interface DecisionTreeParamspublic final IntParam maxBins()
DecisionTreeParamsmaxBins in interface DecisionTreeParamspublic final IntParam minInstancesPerNode()
DecisionTreeParamsminInstancesPerNode in interface DecisionTreeParamspublic final DoubleParam minWeightFractionPerNode()
DecisionTreeParamsminWeightFractionPerNode in interface DecisionTreeParamspublic final DoubleParam minInfoGain()
DecisionTreeParamsminInfoGain in interface DecisionTreeParamspublic final IntParam maxMemoryInMB()
DecisionTreeParamsmaxMemoryInMB in interface DecisionTreeParamspublic final BooleanParam cacheNodeIds()
DecisionTreeParamscacheNodeIds in interface DecisionTreeParamspublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final LongParam seed()
HasSeedpublic final IntParam checkpointInterval()
HasCheckpointIntervalcheckpointInterval in interface HasCheckpointIntervalpublic int depth()
DecisionTreeModeldepth in interface DecisionTreeModelpublic String uid()
Identifiableuid in interface Identifiablepublic Node rootNode()
DecisionTreeModelrootNode in interface DecisionTreeModelpublic int numFeatures()
PredictionModelnumFeatures in class PredictionModel<Vector,DecisionTreeClassificationModel>public int numClasses()
ClassificationModelnumClasses in class ClassificationModel<Vector,DecisionTreeClassificationModel>public double predict(Vector features)
ClassificationModeltransform() and output predictionCol.
This default implementation for classification predicts the index of the maximum value
from predictRaw().
predict in class ClassificationModel<Vector,DecisionTreeClassificationModel>features - (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class ProbabilisticClassificationModel<Vector,DecisionTreeClassificationModel>schema - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
ProbabilisticClassificationModelfeaturesCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector.
transform in class ProbabilisticClassificationModel<Vector,DecisionTreeClassificationModel>dataset - input datasetpublic Vector predictRaw(Vector features)
ClassificationModeltransform() and output rawPredictionCol.
predictRaw in class ClassificationModel<Vector,DecisionTreeClassificationModel>features - (undocumented)public DecisionTreeClassificationModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<DecisionTreeClassificationModel>extra - (undocumented)public String toString()
DecisionTreeModeltoString in interface DecisionTreeModeltoString in interface IdentifiabletoString in class Objectpublic Vector featureImportances()
public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable