Visualizing the invisible: Mitigating business risk with split & compare quantiles (SCQ)
The Split & Compare Quantile Plot is a visualization tool that can aid businesses in decision-making and risk mitigation. This technique enables a comprehensive evaluation of machine learning models across various subsets of data, helping companies optimize their strategies and achieve their goals.
Businesses can take corrective actions and improve their overall performance by identifying areas where their models may be underperforming.
To create a split and compare quantile plot, one can first split the dataset into equal quantiles and then separate the outcomes into favorable and unfavorable categories. For instance, the sample data can be divided into deciles and categorized based on their labels. After dividing the data into equal-sized bins, the percentage of observations in each bin can be computed for both favorable and unfavorable outcomes. This approach is straightforward but efficient, enabling the analysis of a model's performance and facilitating informed business decisions.
Flowchart for Assessing Machine Learning Models with a Split and Compare Quantile Plot
Data Structuring and Grouping
Visualizing the Plot
Providing Explanations
Analyze the plot to determine predicted data/scores in each actual bin
Create a stacked bar plot with actual bins on X-axis and predicted on Y-axis
Gather the actual and predicted values from the trained model
Start
Use the plot to evaluate each prediction granularly
Colour-code bars to differentiate between bins
Group predicted values into equal-sized bins as actual values
Group actual values into bins
Find the percentage of predicted data in each bin of actual data
End
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