public static class ModelMetricsBinomial.MetricBuilderBinomial<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>> extends ModelMetricsSupervised.MetricBuilderSupervised<T>
Modifier and Type | Field and Description |
---|---|
protected AUC2.AUCBuilder |
_auc |
protected double |
_logloss |
_domain, _nclasses
_count, _customMetric, _sumsqe, _wcount, _work, _wY, _wYY
Constructor and Description |
---|
MetricBuilderBinomial(java.lang.String[] domain) |
Modifier and Type | Method and Description |
---|---|
double |
auc() |
void |
cachePrediction(double[] cdist,
Chunk[] chks,
int row,
int cacheChunkIdx,
Model m) |
ModelMetrics |
makeModelMetrics(Model m,
Frame f,
Frame frameWithWeights,
Frame preds)
Create a ModelMetrics for a given model and frame
|
Frame |
makePredictionCache(Model m,
Vec response) |
double[] |
perRow(double[] ds,
float[] yact,
double w,
double o,
Model m) |
double[] |
perRow(double[] ds,
float[] yact,
Model m) |
double |
pr_auc() |
void |
reduce(T mb) |
java.lang.String |
toString() |
postGlobal, postGlobal, setCustomMetric, weightedSigma
asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonString, write, writeExternal, writeJSON
protected double _logloss
protected AUC2.AUCBuilder _auc
public double auc()
public double pr_auc()
public double[] perRow(double[] ds, float[] yact, Model m)
perRow
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
public double[] perRow(double[] ds, float[] yact, double w, double o, Model m)
perRow
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
public void reduce(T mb)
reduce
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
public ModelMetrics makeModelMetrics(Model m, Frame f, Frame frameWithWeights, Frame preds)
makeModelMetrics
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
m
- Modelf
- FrameframeWithWeights
- Frame that contains extra columns such as weightspreds
- Optional predictions (can be null), only used to compute Gains/Lift table for binomial problems @returnpublic Frame makePredictionCache(Model m, Vec response)
makePredictionCache
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
public void cachePrediction(double[] cdist, Chunk[] chks, int row, int cacheChunkIdx, Model m)
cachePrediction
in class ModelMetrics.MetricBuilder<T extends ModelMetricsBinomial.MetricBuilderBinomial<T>>
public java.lang.String toString()
toString
in class java.lang.Object