Comment on page
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?
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?