Links

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.​