public static class KMeansModel.KMeansOutput
extends hex.ClusteringModel.ClusteringOutput
Modifier and Type | Field and Description |
---|---|
double |
_betweenss |
int |
_categorical_column_count |
double[] |
_history_withinss |
int |
_iterations |
int[] |
_k |
double[] |
_reassigned_count |
double |
_tot_withinss |
double |
_totss |
long[] |
_training_time_ms |
double[] |
_withinss |
_centers_raw, _centers_std_raw, _mode, _normMul, _normSub, _size
_column_types, _cross_validation_fold_assignment_frame_id, _cross_validation_holdout_predictions_frame_id, _cross_validation_metrics, _cross_validation_metrics_summary, _cross_validation_models, _cross_validation_predictions, _cv_scoring_history, _defaultThreshold, _distribution, _domains, _end_time, _hasFold, _hasOffset, _hasTreatment, _hasWeights, _isSupervised, _job, _model_summary, _modelClassDist, _names, _orig_projection_array, _origDomains, _origNames, _priorClassDist, _reproducibility_information_table, _run_time, _scoring_history, _start_time, _total_run_time, _training_metrics, _validation_metrics
Constructor and Description |
---|
KMeansOutput(KMeans b) |
getModelCategory, isSupervised, nclasses
changeModelMetricsKey, checksum_impl, classNames, clearModelMetrics, createInputFramesInformationTable, defaultThreshold, features, foldIdx, foldName, getInformationTableNumRows, getModelMetrics, getVariableImportances, hasFold, hasOffset, hasResponse, hasTreatment, hasWeights, interactionBuilder, isAutoencoder, isBinomialClassifier, isClassifier, isMultinomialClassifier, lastSpecialColumnIdx, nfeatures, offsetIdx, offsetName, printTwoDimTables, resetThreshold, responseIdx, responseName, setNames, setNames, startClock, stopClock, toString, treatmentIdx, treatmentName, weightsIdx, weightsName
public int _iterations
public double[] _withinss
public double _tot_withinss
public double[] _history_withinss
public double _totss
public double _betweenss
public int _categorical_column_count
public long[] _training_time_ms
public double[] _reassigned_count
public int[] _k
public KMeansOutput(KMeans b)