The distribution of the model outputs tells us the model's evaluation of how likely the predicted outcome is to happen across the model's population. If the model's population changes, then our populations' actions will be different. The difference in actions is very important to know as soon as possible because they directly affect the business results from operating a machine learning model.
Note
The following example uses timestamps<Timestamp>. These are optional but have an impact on the way data is chunked and results are plotted. You can read more about them in the data requirements<data_requirements_columns_timestamp>.
NannyML detects data drift for Model Outputs using the Univariate Drift Detection methodology<univariate_drift_detection_walkthrough>.
In order to monitor a model, NannyML needs to learn about it from a reference dataset. Then it can monitor the data that is subject to actual analysis, provided as the analysis dataset. You can read more about this in our section on data periods<data-drift-periods>.
Let's start by loading some synthetic data provided by the NannyML package, and setting it up as our reference and analysis dataframes. This synthetic data is for a binary classification model, but multi-class classification can be handled in the same way.
The ~nannyml.drift.model_inputs.univariate.statistical.calculator.StatisticalOutputDriftCalculator class implements the functionality needed for drift detection in model outputs. First, the class is instantiated with appropriate parameters. To check the model outputs for data drift, we only need to pass in the column header of the outputs as y_pred and y_pred_proba.
Then the ~nannyml.drift.model_inputs.univariate.statistical.calculator.StatisticalOutputDriftCalculator.fit method is called on the reference data, so that the data baseline can be established.
Then the ~nannyml.drift.model_inputs.univariate.statistical.calculator.StatisticalOutputDriftCalculator.calculate method calculates the drift results on the data provided. An example using it can be seen below.
We can then display the results in a table, or as plots.
NannyML can show the statistical properties of the drift in model scores as a plot.
NannyML can also visualise how the distributions of the model scores evolved over time.
NannyML can show the statistical properties of the drift in the predicted labels as a plot.
NannyML can also visualise how the distributions of the predicted labels evolved over time.
If required, the Performance Estimation<performance-estimation> functionality of NannyML can help provide estimates of the impact of the observed changes to Model Outputs.