Trained models are trained models that you own.
For a list of methods for this resource, see the end of this page.
Resource representations
{
"kind": "prediction#training",
"id": string,
"storageDataLocation": string,
"storagePMMLLocation": string,
"storagePMMLModelLocation": string,
"modelType": string,
"trainingInstances": [
{
"output": string,
"csvInstance": [
(value)
]
}
],
"selfLink": string,
"utility": [
{
(key): double
}
],
"created": datetime,
"trainingComplete": datetime,
"modelInfo": {
"numberInstances": long,
"modelType": string,
"numberLabels": long,
"classificationAccuracy": double,
"classWeightedAccuracy": double,
"meanSquaredError": double
},
"trainingStatus": string
}
| Property name | Value | Description | Notes |
|---|---|---|---|
kind
|
string
|
What kind of resource this is. | |
id
|
string
|
The unique name for the predictive model.A name for the predictive model, unique within this user account. Naming restrictions are 1-255 characters long, any mix of digits, lowercase letters, dashes, and underscores:
[0-9a-z_\-]
|
|
storageDataLocation
|
string
|
Google storage location of the training data file. | |
storagePMMLLocation
|
string
|
Google storage location of the pre-processing PMML file. See Importing PMML Models for details. | |
storagePMMLModelLocation
|
string
|
Google storage location of the PMML model file. | |
modelType
|
string
|
Type of predictive model (classification or regression) | |
trainingInstances[]
|
list
|
Instances to train model on. | |
trainingInstances[].
output
|
string
|
The generic output value - could be regression or class label | |
trainingInstances[].
csvInstance[]
|
list
|
The input features for this instance | |
selfLink
|
string
|
A URL to re-request this resource. | |
utility[]
|
list
|
[Categorical models only] A class label weighting function, which allows the importance weights for class labels to be specified. See prediction.trainedmodels.insert() for details.
The format of this array is:
[{'label1':val_1},{'label2':val_2}]
where the value is a positive double precision value. Not all labels must be specified; default value for unspecified labels is 1.0. Labels must match example labels exactly. Example:
'utility': [ {'not_spam' : 5}, {'spam' : 1} ]
|
|
utility[].
(key)
|
double
|
||
created
|
datetime
|
Insert time of the model (as a RFC 3339 timestamp). | |
trainingComplete
|
datetime
|
Training completion time (as a RFC 3339 timestamp). | |
modelInfo
|
object
|
Model metadata. | |
modelInfo.
numberInstances
|
long
|
Number of valid data instances used in the trained model. | |
modelInfo.
modelType
|
string
|
Type of predictive model (
CLASSIFICATION
or
REGRESSION
)
|
|
modelInfo.
numberLabels
|
long
|
[Categorical models only] Number of class labels in the trained model. | |
modelInfo.
classificationAccuracy
|
double
|
[Categorical models only]
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.
If you are retraining an existing model, the
modelInfo
field will show an accuracy value in even if the new training is not complete. This number will be the accuracy of the previously trained model, which is still usable, until the new model has finished training.
|
|
modelInfo.
classWeightedAccuracy
|
double
|
[Categorical models only] Estimated accuracy of model taking utility weights into account. | |
modelInfo.
meanSquaredError
|
double
|
[Regression models only] An estimated mean squared error. The can be used to measure the quality of the predicted model. | |
trainingStatus
|
string
|
The current status of the training job. This can be one of following:
|