MinimumAccuracy

Checks if the model’s prediction accuracy meets or surpasses a specified threshold.

Purpose: The Minimum Accuracy test’s objective is to verify whether the model’s prediction accuracy on a specific dataset meets or surpasses a predetermined minimum threshold. Accuracy, which is simply the ratio of right predictions to total predictions, is a key metric for evaluating the model’s performance. Considering binary as well as multiclass classifications, accurate labeling becomes indispensable.

Test Mechanism: The test mechanism involves contrasting the model’s accuracy score with a pre-set minimum threshold value, default value being 0.7. The accuracy score is computed utilizing sklearn’s accuracy_score method, where the true label y_true and predicted label class_pred are compared. If the accuracy score is above the threshold, the test gets a passing mark. The test returns the result along with the accuracy score and threshold used for the test.

Signs of High Risk: - The risk level for this test surges considerably when the model is unable to achieve or surpass the predefined score threshold. - When the model persistently scores below the threshold, it suggests a high risk of inaccurate predictions, which in turn affects the model’s efficiency and reliability.

Strengths: - One of the key strengths of this test is its simplicity, presenting a straightforward measure of the holistic model performance across all classes. - This test is particularly advantageous when classes are balanced. - Another advantage of this test is its versatility as it can be implemented on both binary and multiclass classification tasks.

Limitations: - When analyzing imbalanced datasets, certain limitations of this test emerge. The accuracy score can be misleading when classes in the dataset are skewed considerably. - This can result in favoritism towards the majority class, consequently giving an inaccurate perception of the model performance. - Another limitation is its inability to measure the model’s precision, recall, or capacity to manage false positives or false negatives. - The test majorly focuses on overall correctness and may not be sufficient for all types of model analytics.