com.amazonaws.services.forecast.model

Class WeightedQuantileLoss

• Constructor Detail

• WeightedQuantileLoss

public WeightedQuantileLoss()
• Method Detail

• setQuantile

public void setQuantile(Double quantile)

The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.

Parameters:
quantile - The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.
• getQuantile

public Double getQuantile()

The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.

Returns:
The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.
• withQuantile

public WeightedQuantileLoss withQuantile(Double quantile)

The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.

Parameters:
quantile - The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.
Returns:
Returns a reference to this object so that method calls can be chained together.
• setLossValue

public void setLossValue(Double lossValue)

The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.

Parameters:
lossValue - The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.
• getLossValue

public Double getLossValue()

The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.

Returns:
The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.
• withLossValue

public WeightedQuantileLoss withLossValue(Double lossValue)

The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.

Parameters:
lossValue - The difference between the predicted value and actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.
Returns:
Returns a reference to this object so that method calls can be chained together.
• toString

public String toString()
Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
Overrides:
toString in class Object
Returns:
A string representation of this object.
Object.toString()
• equals

public boolean equals(Object obj)
Overrides:
equals in class Object
• hashCode

public int hashCode()
Overrides:
hashCode in class Object