• API Reference - SingleDatasetPerformance

Single Dataset Performance

This notebook provides an overview for using and understanding the single dataset performance check.

Structure:

• What is the purpose of the check?

• Generate data & model

• Run the check

• Define a condition

• Using a custom scorer

What is the purpose of the check?

This check is designed for evaluating a model’s performance on a labeled dataset based on a scorer or multiple scorers.

Scorers are a convention of sklearn to evaluate a model, it is a function which accepts (model, X, y_true) and returns a float result which is the score. A sklearn convention is that higher scores are better than lower scores. For additional details see scorers documentation.

The default scorers that are used are F1, Precision, and Recall for Classification and Negative Root Mean Square Error, Negative Mean Absolute Error, and R2 for Regression.

Generate data & model

from deepchecks.tabular.datasets.classification.iris import load_data, load_fitted_model

_, test_dataset = load_data()
model = load_fitted_model()

Run the check

You can select which scorers to use by passing either a list or a dict of scorers to the check, see Metrics Guide for additional details.

from deepchecks.tabular.checks import SingleDatasetPerformance

check = SingleDatasetPerformance(scorers=['recall_per_class', 'precision_per_class', 'f1_macro', 'f1_micro'])
result = check.run(test_dataset, model)
result.show()

Define a condition

We can define on our check a condition to validate that the different metric scores are above a certain threshold. Using the class_mode argument we can define select a sub set of the classes to use for the condition.

Let’s add a condition to the check and see what happens when it fails:

check.add_condition_greater_than(threshold=0.85, class_mode='all')
result = check.run(test_dataset, model)
result.show(show_additional_outputs=False)

We detected that the Recall score is below specified threshold in at least one of the classes.

Using a custom scorer

In addition to the built-in scorers, we can define our own scorer based on sklearn api and run it using the check alongside other scorers:

from sklearn.metrics import fbeta_score, make_scorer

fbeta_scorer = make_scorer(fbeta_score, labels=[0, 1, 2], average=None, beta=0.2)

check = SingleDatasetPerformance(scorers={'my scorer': fbeta_scorer, 'recall': 'recall_per_class'})
result = check.run(test_dataset, model)
result.show()

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Regression Systematic Error

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Train Test Performance