ScorecardBucketHistogram

Evaluates and visualizes distribution of risk categories in a classification model’s scores, useful in credit risk assessment.

Purpose: The ‘Scorecard Bucket Histogram’ is employed as a metric to evaluate the performance of a classification model, specifically in credit risk assessment. It categorizes model scores into different rating classes, and visualizes the distribution of scores or probabilities within each class. It essentially measures how different risk categories (classes) are distributed in the model scores and provides insight into the model’s classification ability. This makes it particularly useful in credit scoring and risk modeling where understanding the probability of default is critical.

Test Mechanism: The test works by computing the probabilities for each record in the test and train dataset using the model’s predict function. Subsequently, it calculates the scores using a formula incorporating target score, target odds, and points to double odds (PDO). The scores are then bucketed into predefined rating classes (such as ‘A’, ‘B’, ‘C’, ‘D’) and plotted in a histogram for both the train and test datasets. The target score, target odds, points to double the odds (PDO), and rating classes are customizable parameters, providing flexibility in test metrics based on differing model or industry norms.

Signs of High Risk:

• Disproportionate scores within rating classes
• Excessive overlap between classes
• Inconsistent distribution of scores between the training and testing datasets

If the model is accurately classifying and risk is being evenly distributed, we would anticipate smooth and relatively balanced histograms within classes.

Strengths:

• Provides a quick visual snapshot of score distribution
• Breaks down complex predictions into simple, understandable classes, making it easily interpretable for both technical and non-technical audiences
• Caters to customization of parameters
• Gives ownership of the class definitions to the user
• Useful in the field of credit risk, providing a clear understanding of which class or ‘bucket’ a potential borrower belongs to

Limitations:

• Relies on manual setting of classes and other parameters (like target score, target odds, and PDO), potentially leading to arbitrary classifications and potential bias if not judiciously performed
• Effectiveness can be limited with non-tabular data
• Doesn’t provide a numerical value easily compared across different models or runs as the output is primarily visual
• Might not present a complete view of model performance and should be used in conjunction with other metrics