Multiclass Classification
Multiclass classification models predict one of more than two outcomes. In Aporia, these models are represented with the
multiclass model type.Examples of multiclass classification problems:
- Is this product a book, movie, or clothing?
- Is this movie a romantic comedy, documentary, or thriller?
- Which category of products is most interesting to this customer?
Frequently, multiclass models output a confidence value or a score for each class.
To monitor a multiclass model, create a new model version with a
string field representing the predicted class, and optionally a dict field with the probabilities for all classes:apr_model = aporia.create_model_version(
model_id="<MODEL_ID>",
model_version="v1",
model_type="multiclass"
features={
...
},
predictions={
"product_type": "string",
"proba": "dict"
},
)
Next, connect to a data source or manually log predictions like so:
apr_model.log_prediction(
id="<PREDICTION_ID>",
features={
...
},
predictions={
"product_type": "book",
"proba": {
"book": 0.8,
"movie": 0.1,
"clothing": 0.1
}
},
)
To log actuals for this prediction:
apr_model.log_actuals(
id="<PREDICTION_ID>",
actuals={
"product_type": "book",
"proba": {
"book": 1.0,
"movie": 0.0,
"clothing": 0.0,
},
},
)
If you don't need to monitor probabilities, you may omit the
proba field.