Cloud AutoML V1 API - Class Google::Cloud::AutoML::V1::TextSentimentEvaluationMetrics (v1.5.1)

Reference documentation and code samples for the Cloud AutoML V1 API class Google::Cloud::AutoML::V1::TextSentimentEvaluationMetrics.

Model evaluation metrics for text sentiment problems.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#confusion_matrix

def confusion_matrix() -> ::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfusionMatrix
Returns

#confusion_matrix=

def confusion_matrix=(value) -> ::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfusionMatrix
Parameter
Returns

#f1_score

def f1_score() -> ::Float
Returns
  • (::Float) — Output only. The harmonic mean of recall and precision.

#f1_score=

def f1_score=(value) -> ::Float
Parameter
  • value (::Float) — Output only. The harmonic mean of recall and precision.
Returns
  • (::Float) — Output only. The harmonic mean of recall and precision.

#linear_kappa

def linear_kappa() -> ::Float
Returns
  • (::Float) — Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#linear_kappa=

def linear_kappa=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
Returns
  • (::Float) — Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#mean_absolute_error

def mean_absolute_error() -> ::Float
Returns
  • (::Float) — Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#mean_absolute_error=

def mean_absolute_error=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
Returns
  • (::Float) — Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#mean_squared_error

def mean_squared_error() -> ::Float
Returns
  • (::Float) — Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#mean_squared_error=

def mean_squared_error=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
Returns
  • (::Float) — Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#precision

def precision() -> ::Float
Returns
  • (::Float) — Output only. Precision.

#precision=

def precision=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Precision.
Returns
  • (::Float) — Output only. Precision.

#quadratic_kappa

def quadratic_kappa() -> ::Float
Returns
  • (::Float) — Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#quadratic_kappa=

def quadratic_kappa=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
Returns
  • (::Float) — Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

#recall

def recall() -> ::Float
Returns
  • (::Float) — Output only. Recall.

#recall=

def recall=(value) -> ::Float
Parameter
  • value (::Float) — Output only. Recall.
Returns
  • (::Float) — Output only. Recall.