Loss

```
public interface Loss
```
Regression loss function.

Nested Class Summary

Nested Classes
Modifier and Type Interface and Description

static class Loss.Type
The type of loss.

Nested Classes
Modifier and Type	Interface and Description
`static class`	`Loss.Type` The type of loss.

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method and Description
`static Loss`	`huber(double p)` Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficiency for normally distributed errors.
`double`	`intercept(double[] y)` Returns the intercept of model.
`static Loss`	`lad()` Least absolute deviation regression.
`static Loss`	`logistic(int[] labels)` Logistic regression loss for binary classification.
`static Loss`	`logistic(int c, int k, int[] labels, double[][] p)` Logistic regression loss for multi-class classification.
`static Loss`	`ls()` Least squares regression.
`static Loss`	`ls(double[] y)` Least squares regression.
`double`	`output(int[] nodeSamples, int[] sampleCount)` Calculate the node output.
`static Loss`	`quantile(double p)` Quantile regression.
`double[]`	`residual()` Returns the residual vector.
`double[]`	`response()` Returns the response variable for next iteration.
`static Loss`	`valueOf(java.lang.String s)` Parses the loss.

- Method Detail
  - output
```
double output(int[] nodeSamples,
              int[] sampleCount)
```
    Calculate the node output.
    
    Parameters:
    
    nodeSamples - the index of node samples to their original locations in training dataset.
    
    sampleCount - samples[i] is the number of sampling of dataset[i]. 0 means that the datum is not included and values of greater than 1 are possible because of sampling with replacement.
    
    Returns:
    
    the node output
  - intercept
```
double intercept(double[] y)
```
    Returns the intercept of model.
    
    Parameters:
    
    y - the response variable.
  - response
```
double[] response()
```
    Returns the response variable for next iteration.
  - residual
```
double[] residual()
```
    Returns the residual vector.
  - ls
```
static Loss ls()
```
    Least squares regression. Least-squares is highly efficient for normally distributed errors but is prone to long tails and outliers.
  - ls
```
static Loss ls(double[] y)
```
    Least squares regression. Least-squares is highly efficient for normally distributed errors but is prone to long tails and outliers.
  - quantile
```
static Loss quantile(double p)
```
    Quantile regression. The gradient tree boosting based on this loss function is highly robust. The trees use only order information on the input variables and the pseudo-response has only two values {-1, +1}. The line searches (terminal node values) use only specified quantile ratio.
    
    Parameters:
    
    p - the percentile.
  - lad
```
static Loss lad()
```
    Least absolute deviation regression. The gradient tree boosting based on this loss function is highly robust. The trees use only order information on the input variables and the pseudo-response has only two values {-1, +1}. The line searches (terminal node values) use only medians. This is a special case of quantile regression of q = 0.5.
  - huber
```
static Loss huber(double p)
```
    Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficiency for normally distributed errors.
    
    Parameters:
    
    p - of residuals
  - logistic
```
static Loss logistic(int[] labels)
```
    Logistic regression loss for binary classification.
    
    Parameters:
    
    labels - the class labels.
  - logistic
```
static Loss logistic(int c,
                     int k,
                     int[] labels,
                     double[][] p)
```
    Logistic regression loss for multi-class classification.
    
    Parameters:
    
    c - the class id that this loss function fits on.
    
    k - the number of classes.
    
    labels - the class labels.
    
    p - the posteriori probabilities.
  - valueOf
```
static Loss valueOf(java.lang.String s)
```
    Parses the loss.

Interface Loss

Nested Class Summary

Method Summary

Method Detail

output

intercept

response

residual

ls

ls

quantile

lad

huber

logistic

logistic

valueOf