An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below:What is the most likely reason that the model fails to converge?
Answer(s): A
An analyst knows that the categorical predictor, storeId, is an important predictor of the target.However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.What are the two most effective ways to address the problem? (Choose two.)
Answer(s): B,C
Including redundant input variables in a regression model can:
Answer(s): B
An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model. The analyst discovers that the probability of purchasing a certain item when Region = A is 1. What problem does this illustrate?
Answer(s): C
Refer to the following exhibit: What is a correct interpretation of this graph?
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