Figure 5
A “S H A P” summary plot displays the feature importance for a machine learning model.The vertical axis lists financial features: “Inventories,” “Net Profit,” “Inventory Turnover,” “Share Capital,” “Taxes,” “Days Payable Outstanding,” “Tangibles,” “Total Value Production,” “Days Sales Outstanding,” “Sales,” “Financial Income,” “Operating Result,” “Accounts Payables,” “Current Activities,” “Intangibles,” “Accounts Receivables,” “Current Liabilities,” “Debts,” “Equity,” and “Cash and Cash Equivalent.” The horizontal axis represents the “S H A P value (impact on model output),” ranging from approximately negative 0.6 to 0.8 in increments of 0.2 units, with zero at the center, indicating the impact of each feature on the model‘s output. A color bar on the right, labeled “Feature value,” maps feature values from blue (“Low”) to red (“High”). Each feature has a horizontal distribution of dots, colored according to feature value. “Inventories” and “Net Profit” show the largest magnitude of “S H A P” values, indicating the highest impact, with high values (red dots) for “Inventories” mostly leading to positive impacts. “Inventory Turnover” and “Days Sales Outstanding” also show significant impacts in both directions. All remaining features have narrow distributions centered around zero.

Global summary plot

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