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 correct 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 preset minimum threshold value, with the default being 0.7. The accuracy score is computed utilizing sklearn’s accuracy_score method, where the true labels y_true and predicted labels class_pred are compared. If the accuracy score is above the threshold, the test receives a passing mark. The test returns the result along with the accuracy score and threshold used for the test.

Signs of High Risk

• Model fails to achieve or surpass the predefined score threshold.
• Persistent scores below the threshold, indicating a high risk of inaccurate predictions.

Strengths

• Simplicity, presenting a straightforward measure of holistic model performance across all classes.
• Particularly advantageous when classes are balanced.
• Versatile, as it can be implemented on both binary and multiclass classification tasks.

Limitations

• Misleading accuracy scores when classes in the dataset are highly imbalanced.
• Favoritism towards the majority class, giving an inaccurate perception of model performance.
• Inability to measure the model’s precision, recall, or capacity to manage false positives or false negatives.
• Focused on overall correctness and may not be sufficient for all types of model analytics.