validmind.TargetRateBarPlots
TargetRateBarPlots
@tags('tabular_data', 'visualization', 'categorical_data')
@tasks('classification')
defTargetRateBarPlots(dataset:validmind.vm_models.VMDataset):
Generates bar plots visualizing the default rates of categorical features for a classification machine learning model.
Purpose
This test, implemented as a metric, is designed to provide an intuitive, graphical summary of the decision-making patterns exhibited by a categorical classification machine learning model. The model's performance is evaluated using bar plots depicting the ratio of target rates—meaning the proportion of positive classes—for different categorical inputs. This allows for an easy, at-a-glance understanding of the model's accuracy.
Test Mechanism
The test involves creating a pair of bar plots for each categorical feature in the dataset. The first plot depicts the frequency of each category in the dataset, with each category visually distinguished by its unique color. The second plot shows the mean target rate of each category (sourced from the "default_column"). Plotly, a Python library, is used to generate these plots, with distinct plots created for each feature. If no specific columns are selected, the test will generate plots for each categorical column in the dataset.
Signs of High Risk
- Inconsistent or non-binary values in the "default_column" could complicate or render impossible the calculation of average target rates.
- Particularly low or high target rates for a specific category might suggest that the model is misclassifying instances of that category.
Strengths
- This test offers a visually interpretable breakdown of the model's decisions, providing an easy way to spot irregularities, inconsistencies, or patterns.
- Its flexibility allows for the inspection of one or multiple columns, as needed.
Limitations
- The readability of the bar plots drops as the number of distinct categories increases in the dataset, which can make them harder to understand and less useful.