When creating a story in Einstein Discovery, a client is wondering if all potential collinear fields need to be removed before executing the build story. What would accurately answer the client's question?
A.
Yes. If the collinear variables are not removed, the Einstein Discovery model build will fail.
B.
Yes. If all collinear variables are not excluded, the model will overfit and not make any sense.
C.
No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build, and the ridge regression will prevent collinearity from overfitting.
D.
No. Einstein Discovery is impervious to collinearity, so the story and subsequent model will be fine.
C is the correct answer.
Overfitting
In predictive analytics, overfitting occurs when a model performs well in predicting outcomes on the training data in the dataset, but less well when predicting outcomes for other data, such as production data. Using too many explanatory variables can result in an overly complex predictive model that captures the noise in your data. To mitigate overfitting, Einstein Discovery uses ridge regression and regularization.
You will receive a warning, but can still continue executing your build story.
https://help.salesforce.com/s/articleView?id=sf.bi_edd_glossary.htm&type=5
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