@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class TrainingMetrics extends Object implements Serializable, Cloneable, StructuredPojo
The training metric details.
Constructor and Description |
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TrainingMetrics() |
Modifier and Type | Method and Description |
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TrainingMetrics |
clone() |
boolean |
equals(Object obj) |
Float |
getAuc()
The area under the curve.
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List<MetricDataPoint> |
getMetricDataPoints()
The data points details.
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int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setAuc(Float auc)
The area under the curve.
|
void |
setMetricDataPoints(Collection<MetricDataPoint> metricDataPoints)
The data points details.
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String |
toString()
Returns a string representation of this object.
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TrainingMetrics |
withAuc(Float auc)
The area under the curve.
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TrainingMetrics |
withMetricDataPoints(Collection<MetricDataPoint> metricDataPoints)
The data points details.
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TrainingMetrics |
withMetricDataPoints(MetricDataPoint... metricDataPoints)
The data points details.
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public void setAuc(Float auc)
The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
auc
- The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across
all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect
model has a score of 1.0.public Float getAuc()
The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
public TrainingMetrics withAuc(Float auc)
The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
auc
- The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across
all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect
model has a score of 1.0.public List<MetricDataPoint> getMetricDataPoints()
The data points details.
public void setMetricDataPoints(Collection<MetricDataPoint> metricDataPoints)
The data points details.
metricDataPoints
- The data points details.public TrainingMetrics withMetricDataPoints(MetricDataPoint... metricDataPoints)
The data points details.
NOTE: This method appends the values to the existing list (if any). Use
setMetricDataPoints(java.util.Collection)
or withMetricDataPoints(java.util.Collection)
if you
want to override the existing values.
metricDataPoints
- The data points details.public TrainingMetrics withMetricDataPoints(Collection<MetricDataPoint> metricDataPoints)
The data points details.
metricDataPoints
- The data points details.public String toString()
toString
in class Object
Object.toString()
public TrainingMetrics clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.