Class: Google::Apis::PredictionV1_5::Training::ModelInfo

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
generated/google/apis/prediction_v1_5/classes.rb,
generated/google/apis/prediction_v1_5/representations.rb,
generated/google/apis/prediction_v1_5/representations.rb

Overview

Model metadata.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ ModelInfo

Returns a new instance of ModelInfo



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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 635

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#class_weighted_accuracy ⇒ Float

Estimated accuracy of model taking utility weights into account [Categorical models only]. Corresponds to the JSON property classWeightedAccuracy

Returns:

  • (Float) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 602

def class_weighted_accuracy
  @class_weighted_accuracy
end

#classification_accuracy ⇒ Float

A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only] . Corresponds to the JSON property classificationAccuracy

Returns:

  • (Float) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 612

def classification_accuracy
  @classification_accuracy
end

#mean_squared_error ⇒ Float

An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. Corresponds to the JSON property meanSquaredError

Returns:

  • (Float) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 618

def mean_squared_error
  @mean_squared_error
end

#model_type ⇒ String

Type of predictive model (CLASSIFICATION or REGRESSION) Corresponds to the JSON property modelType

Returns:

  • (String) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 623

def model_type
  @model_type
end

#number_instances ⇒ Fixnum

Number of valid data instances used in the trained model. Corresponds to the JSON property numberInstances

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 628

def number_instances
  @number_instances
end

#number_labels ⇒ Fixnum

Number of class labels in the trained model [Categorical models only]. Corresponds to the JSON property numberLabels

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 633

def number_labels
  @number_labels
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'generated/google/apis/prediction_v1_5/classes.rb', line 640

def update!(**args)
  @class_weighted_accuracy = args[:class_weighted_accuracy] if args.key?(:class_weighted_accuracy)
  @classification_accuracy = args[:classification_accuracy] if args.key?(:classification_accuracy)
  @mean_squared_error = args[:mean_squared_error] if args.key?(:mean_squared_error)
  @model_type = args[:model_type] if args.key?(:model_type)
  @number_instances = args[:number_instances] if args.key?(:number_instances)
  @number_labels = args[:number_labels] if args.key?(:number_labels)
end