Enum gbdt::config::Loss

pub enum Loss {
    SquaredError,
    LogLikelyhood,
    LAD,
    RegLinear,
    RegLogistic,
    BinaryLogistic,
    BinaryLogitraw,
    MultiSoftprob,
    MultiSoftmax,
    RankPairwise,
}

This enum defines the loss type.

We support three loss types for training and inference:

1. SquaredError for regression. The label and the predicted value will be a float number.
2. LogLikelyhood for binary classification. The label value should be -1 or 1. The predicted value should be a float number between 0 and 1, which is the possibility of label 1.
3. LAD for regression. The label and the predicted value will be a float number.

Note that LogLikelyhood only support binary classification.

We also suppot seven objectives from Xgboost for inference. See xgboost

1. RegLinear ("reg:linear" in xgboost): linear regression.
2. RegLogistic ("reg:logistic" in xgboost): logistic regression.
3. BinaryLogistic ("binary:logistic" in xgboost): logistic regression for binary classification, output probability
4. BinaryLogitraw ("binary:logitraw" in xgboost): logistic regression for binary classification, output score before logistic transformation
5. MultiSoftprob ("multi:softprob" in xgboost): multiclass classification. Call gbdt::predict_multiclass to get the predictions.
6. MultiSoftmax ("multi:softmax" in xgboost): multiclass classification. Call gbdt::predict_multiclass to get the predictions.
7. RankPairwise ("rank:pairwise" in xgboost): pairwise rank. See xgboost's demo

Variants

SquaredError

SquaredError ("SquaredError") for regression. The label and the predicted value will be a float number.

LogLikelyhood

LogLikelyhood ("LogLikelyhood") for binary classification. The label value should be -1 or 1. The predicted value should be a float number between 0 and 1, which is the possibility of label 1.

LAD

LAD ("LAD") for regression. The label and the predicted value will be a float number.

RegLinear

RegLinear ("reg:linear") from Xgboost: linear regression.

RegLogistic

RegLogistic ("reg:logistic") from Xgboost: logistic regression.

BinaryLogistic

BinaryLogistic ("binary:logistic") from Xgboost: logistic regression for binary classification, output probability

BinaryLogitraw

BinaryLogitraw ("binary:logitraw") from Xgboost: logistic regression for binary classification, output score before logistic transformation

MultiSoftprob

MultiSoftprob ("multi:softprob") from Xgboost: multiclass classification. Call gbdt::predict_multiclass to get the predictions.

MultiSoftmax

MultiSoftmax ("multi:softmax") from Xgboost: multiclass classification. Call gbdt::predict_multiclass to get the predictions.

RankPairwise

RankPairwise ("rank:pairwise") from Xgboost: pairwise rank. See xgboost's demo

Trait Implementations

impl Default for Loss

fn default() -> Self

SquaredError are used as default loss type.

impl Clone for Loss

fn clone(&self) -> Loss

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl PartialEq<Loss> for Loss

fn eq(&self, other: &Loss) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]

fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl Debug for Loss

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for Loss

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<'de> Deserialize<'de> for Loss

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.