Feature importance plots (IMAGE)
Caption
It displays the feature importance graphs derived from the Random Forest and the Extra Trees models. Feature importance is measured by each feature’s percentage of total predictive power; a higher feature importance indicates stronger predictive power for that variable. The black solid lines within each bar represent the standard deviation of the importance value for each variable of interest.
The figure indicates that the amount of fixed assets (i.e. property, plant, and equipment), industry, and the number of employees are the three most important variables for the ET model. Conversely, total assets is the least important variable, likely because the model already includes the amount of fixed assets and non-current assets.
Credit
Lucas S. Li (Shanghai American School, China) Yan Zhao (City College of the City University of New York, USA)
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License
Original content