public class ModelMetricsBinomialUplift extends ModelMetricsSupervised
Modifier and Type | Class and Description |
---|---|
static class |
ModelMetricsBinomialUplift.MetricBuilderBinomialUplift |
ModelMetricsSupervised.MetricBuilderSupervised<T extends ModelMetricsSupervised.MetricBuilderSupervised<T>>
ModelMetrics.MetricBuilder<T extends ModelMetrics.MetricBuilder<T>>
Modifier and Type | Field and Description |
---|---|
AUUC |
_auuc |
_domain, _sigma
_custom_metric, _description, _MSE, _nobs, _scoring_time
Constructor and Description |
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ModelMetricsBinomialUplift(Model model,
Frame frame,
long nobs,
java.lang.String[] domain,
double sigma,
AUUC auuc,
CustomMetric customMetric) |
Modifier and Type | Method and Description |
---|---|
protected java.lang.StringBuilder |
appendToStringMetrics(java.lang.StringBuilder sb) |
double |
auuc() |
double |
auucNormalized() |
static ModelMetricsBinomialUplift |
getFromDKV(Model model,
Frame frame) |
static ModelMetricsBinomialUplift |
make(Vec targetClassProbs,
Vec actualLabels,
Vec treatment,
AUUC.AUUCType auucType,
int nbins)
Build a Binomial ModelMetrics object from target-class probabilities, from actual labels, and a given domain for both labels (and domain[1] is the target class)
|
static ModelMetricsBinomialUplift |
make(Vec targetClassProbs,
Vec actualLabels,
Vec treatment,
java.lang.String[] domain,
AUUC.AUUCType auucType,
int auucNbins)
Build a Binomial ModelMetrics object from target-class probabilities, from actual labels, and a given domain for both labels (and domain[1] is the target class)
|
int |
nbins() |
double |
qini() |
java.lang.String |
toString() |
r2
auc_obj, buildKey, buildKey, calcVarImp, calcVarImp, calcVarImp, calcVarImp, checksum_impl, cm, deepCloneWithDifferentModelAndFrame, defaultModelMetrics, frame, getAllowedMetrics, getMetricFromModel, getMetricFromModelMetric, hr, isForFrame, isForModel, model, mse, residual_degrees_of_freedom, rmse, setModelKey, sortModelsByMetric, sortModelsByMetric, withDescription, withModelAndFrame
checksum_impl, checksum, checksum, getKey, makeSchema, readAll_impl, readAll, remove_impl, remove_impl, remove_self_key_impl, remove, remove, remove, remove, remove, remove, removeQuietly, writeAll_impl, writeAll
asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonBytes, toJsonString, write, writeExternal, writeJSON
public final AUUC _auuc
public ModelMetricsBinomialUplift(Model model, Frame frame, long nobs, java.lang.String[] domain, double sigma, AUUC auuc, CustomMetric customMetric)
public static ModelMetricsBinomialUplift getFromDKV(Model model, Frame frame)
public java.lang.String toString()
toString
in class ModelMetricsSupervised
public double auuc()
public double qini()
public double auucNormalized()
public int nbins()
protected java.lang.StringBuilder appendToStringMetrics(java.lang.StringBuilder sb)
appendToStringMetrics
in class ModelMetrics
public static ModelMetricsBinomialUplift make(Vec targetClassProbs, Vec actualLabels, Vec treatment, AUUC.AUUCType auucType, int nbins)
targetClassProbs
- A Vec containing target class probabilitiesactualLabels
- A Vec containing the actual labels (can be for fewer labels than what's in domain, since the predictions can be for a small subset of the data)public static ModelMetricsBinomialUplift make(Vec targetClassProbs, Vec actualLabels, Vec treatment, java.lang.String[] domain, AUUC.AUUCType auucType, int auucNbins)
targetClassProbs
- A Vec containing target class probabilitiesactualLabels
- A Vec containing the actual labels (can be for fewer labels than what's in domain, since the predictions can be for a small subset of the data)treatment
- A Vec containing the treatment valuesdomain
- The two class labels (domain[0] is the non-target class, domain[1] is the target class, for which probabilities are given)auucType
- Type of default AUUCauucNbins
- Number of bins to calculate AUUC (-1 means default value 1000, the number has to be higher than zero)