Regression

Regression models predict a numeric value. In Aporia, these models are represented with the regression model type.

Examples of regression problems:

• What will the temperature be in Seattle tomorrow?

• For product X, how many units will sell?

• How many days until this customer stops using the application?

• What price will this house sell for?

Integration

Regression predictions are usually represented in a database with a numeric column. For example:

id	feature1 (numeric)	feature2 (boolean)	predicted_temperature (numeric)	actual_temperature (numeric)	timestamp (datetime)
1	13.5	True	22.83	24.12	2017-01-01 12:00:00
2	123	False	26.04	25.99	2017-01-01 12:01:00
3	42	True	29.01	11.12	2017-01-01 12:02:00

To monitor this model, we will create a new model version with a schema that includes a numeric prediction:

apr_model = aporia.create_model_version(
  model_id="<MODEL_ID>", # You will need to create a model with this MODEL_ID in advance
  model_version="v1",
  model_type="regression"
  features={
     ...
  },
  predictions={
    "predicted_temperature": "numeric",
  },
)

To connect this model to Aporia from your data source, call the connect_serving(...) API:

apr_model.connect_serving(
  data_source=my_data_source,

  id_column="id",
  timestamp_column="timestamp",

  # Map the actual_temperature column as the label for the 
  # predicted_temperature. 
  labels={
    # Prediction name -> Column name
    "predicted_temperature": "actual_prediction"
  }
)

Check out the data sources section for more information about how to connect all other available data sources.

Don't want to connect to a database?

Don't worry - you can log your predictions directly to Aporia.