Top Prediction Attributes
Learn more about the employee attributes that had the strongest impact on prediction scores.
For each prediction, the algorithm generates a list of attributes that had the strongest impact on the score. These attributes provide insight into why an employee has a high prediction likelihood and can be used as a starting point for an intervention.
Associated prediction scores
For the top attributes an associated prediction score is calculated by averaging the historical event likelihoods across trees where the attribute was used in the prediction. This is the prediction score if the attribute alone was the most important attribute for the prediction.
When comparing the associated prediction to the actual prediction:
- If the associated prediction is greater than the actual prediction, the attribute is increasing the prediction value.
- If the associated prediction is lower than the actual prediction, the attribute is decreasing the prediction value.
Note: It is not possible to fully explain a prediction based on the top prediction attributes because the result is not based on a list of independently contributing attributes, but on the combination of attributes that were historically most impacting the occurrence of events.
The following is a list of Floyd's top resignation risk attributes:
| Attribute | Value |
|---|---|
| Tenure | 0.17 yrs |
| Location | Canada |
| Organization | Finance |
| Base pay | 44.52K |
| Training hours | 0 hrs |
These attributes were used most often to predict Floyd's risk of resignation, as shown in the following illustration.
The associated risk of resignation for these attributes is calculated by averaging the historical event likelihoods across the trees where the attribute was used in the prediction, as shown in the following illustration.
