Returns information about a trained model, including training status, confusion matrix, and estimated error values.

Note that this will not return successfully for a new model until training has completed successfully.

• If this is an attempt to train a new model, trainedmodels.get will return HTTP 404 "No model found. Training running." until training completes successfully or not. If training completes successfully, the method will return the trainedmodels resource; if training fails, this method will return an HTTP 404 "No model found. Model must first be trained"
• If this is an attempt to retrain an existing model, trainedmodels.get will always return a trainedmodels resource. If the retraining succeeds, the resource will be for the new model. If the retraining fails, the resource will be for the previous model, but the trainingStatus property value will be ERROR .

Important: Only the user who trained a model can call this method.

Confusion Matrices

This method returns a modelInfo.confusionMatrix property that describes a confusion matrix . This matrix describes how many labels were properly and improperly guessed for each training entry during training. This is useful for evaluating the accuracy of training over your data; if the matrix indicates that specific values are often confused, you might want to change your training data structure.

Here is an example confusion matrix for a language identification model. In this model, for all entries with the label "French", 12 were properly identified as French and 0.5 were improperly identified as English. You can see the values for items labeled "Spanish" and "English" as well. Numbers can be fractions because they are averaged across multiple training runs. confusionMatrixRowTotals describes the total number of each label applied.

"confusionMatrix": {
   "French": {
    "French": 12.0,
    "English": 0.5
   },
   "Spanish": {
    "Spanish": 6.0,
    "English": 1.0
   },
   "English": {
    "French": 0.5,
    "Spanish": 2.0,
    "English": 20.0
   }
  },
  "confusionMatrixRowTotals": {
   "French": 12.5,
   "Spanish": 7.0,
   "English": 22.5
  }
 }

GET https://www.googleapis.com/prediction/v1.4/trainedmodels/{id}

Invoking this method requires the use of a token with access to:
https://www.googleapis.com/auth/prediction

Asynchronous call to start training a model. When training has completed succesfully, prediction.trainedmodels.get() will return information about the training model. Training can take up to several hours, depending on the complexity and size of the data, but will typically take less time.

Note that each time you call this method, it will overwrite any existing model with the same ID if training succeeds. If training fails, the existing model will not be replaced. You can continue to run queries against an existing model during the training of a new model.

In order to train against data stored in Google Cloud Storage, you must have read or owner rights on that data. You must have read permission on the Google Cloud Storage object that holds your training data. By default, when you upload a file, you are assigned owner rights. When you train against that model, your training method call must be authenticated to a user with read rights.

Note that a model can be used only by the user who calls trainedmodels.insert to create that model.

Learn more about modifying Google Cloud Storage object ACLs.

Authentication is required.

POST https://www.googleapis.com/prediction/v1.4/trainedmodels
{
  "id": string,
  "storageDataLocation": string,
  "storagePMMLLocation": string,  // Only used for PMML preprocessing
  "utility": [                    // Optional, categorical models only
    {
      any value: double
    }
  ]
}

{
  "error": {
    "errors": [{
    "domain": "global",
    "reason": "notFound",
    "message": "No model found. Model must first be trained."
    }],
    "code": 404,
    "message": "No model found. Model must first be trained."
  }
}

Invoking this method requires the use of a token with access to:
https://www.googleapis.com/auth/prediction

[ Categorization models only ] Add new data to a trained model.

Adding new data to a trained model is called streaming training . Streaming training trains a previously trained model against a new example. This is useful if you have a regular stream of new information that you'd like to add to your model as it becomes available, rather than having to recompile, re-upload, and retrain the data with batches of new data. The model is not retrained each time it receives a new example; rather, it retrains after every N new examples have been added, where N is a small number.

Note that the system may weight newer streamed examples more than earlier examples. If you do not want this, you should add the examples to your training data and retrain the system against all the data by calling prediction.trainedmodels.insert() .

Note: If you retrain a model against its original training data file, all the streamed data will be lost. If you want to retain the streamed data, you must store it and update the model data yourself.

Authentication is required.

PUT https://www.googleapis.com/prediction/v1.4/trainedmodels/{id}
{
  "label" : my_label
  "csvInstance: [ col1, col2....colN ]
}

Invoking this method requires the use of a token with access to:
https://www.googleapis.com/auth/prediction

Delete a trained model.

Authentication is required.

DELETE https://www.googleapis.com/prediction/v1.4/trainedmodels/{id}

Invoking this method requires the use of a token with access to:
https://www.googleapis.com/auth/prediction

Run a prediction against your model.

Here's an example request to a hosted model that predicts a person's height, if the model expects a string gender ("M" or "F"), two height numbers, and a string country name:

{
  "input":{
    "csvInstance":["M", 1.59, 1.51,"France"]
  }
}

Authentication is required.

POST https://www.googleapis.com/prediction/v1.4/trainedmodels/{id}/predict

{
  "input":{
    "csvInstance":[ col1_value, col2_value, ... ]
  }
}

{
  "kind": "prediction#output",
  "id": string,
  "selfLink": string,
  "outputLabel": string,
  "outputMulti": [
    {
      "label": string,
      "score": double
    }
  ],
  "outputValue": double
}

Invoking this method requires the use of a token with access to:
https://www.googleapis.com/auth/prediction