BqmlIterationResult class
Constructors
-
BqmlIterationResult({Input<String>? durationMs, Input<double>? evalLoss, Input<int>? index, Input<double>? learnRate, Input<double>? trainingLoss})
-
Creates a new BqmlIterationResult.
durationMs
Output-only, Beta Time taken to run the training iteration in milliseconds.
evalLoss Output-only, Beta Eval loss computed on the eval data at the end of the iteration. The eval loss is used for early stopping to avoid overfitting. No eval loss if eval_split_method option is specified as no_split or auto_split with input data size less than 500 rows.
index Output-only, Beta Index of the ML training iteration, starting from zero for each training run.
learnRate Output-only, Beta Learning rate used for this iteration, it varies for different training iterations if learn_rate_strategy option is not constant.
trainingLoss Output-only, Beta Training loss computed on the training data at the end of the iteration. The training loss function is defined by model type.
const
-
BqmlIterationResult.fromMap(Map<String, dynamic> map)
-
factory
Properties
-
durationMs
→ Input<String>?
-
Output-only, Beta Time taken to run the training iteration in milliseconds.
final
-
evalLoss
→ Input<double>?
-
Output-only, Beta Eval loss computed on the eval data at the end of the iteration. The eval loss is used for early stopping to avoid overfitting. No eval loss if eval_split_method option is specified as no_split or auto_split with input data size less than 500 rows.
final
-
hashCode
→ int
-
The hash code for this object.
no setterinherited
-
index
→ Input<int>?
-
Output-only, Beta Index of the ML training iteration, starting from zero for each training run.
final
-
learnRate
→ Input<double>?
-
Output-only, Beta Learning rate used for this iteration, it varies for different training iterations if learn_rate_strategy option is not constant.
final
-
runtimeType
→ Type
-
A representation of the runtime type of the object.
no setterinherited
-
trainingLoss
→ Input<double>?
-
Output-only, Beta Training loss computed on the training data at the end of the iteration. The training loss function is defined by model type.
final
