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:
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.
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