You need to implement a logistic regression model in SAS to predict customer churn. During model development, you have to ensure the model accounts for the effects of multicollinearity. Which PROC LOGISTIC option should you use to diagnose multicollinearity and provide a measure to identify problematic variables?

You have a decision tree with a depth of 4. During validation, you observe the following characteristics: Leaf nodes at the lower depths have higher confidence for the predicted class but represent a smaller portion of the dataset. Conversely, upper-level nodes cover more of the dataset but with lower confidence levels. Given this scenario, which of the following strategies might help in improving the model’s performance without overfitting?

A data scientist is evaluating the performance of a neural network model for a regression problem. After training, the scientist wants to assess the model's ability to generalize to new data. Which of the following metrics is the most appropriate for evaluating the performance of the neural network on a validation dataset?

When preparing data for a predictive modeling project, a data scientist notices that the categorical variable 'payment_type' with four categories ('credit card', 'debit card', 'paypal', 'other') exhibits a high degree of variability in the outcome variable (purchase amount). To improve the model's predictive accuracy, what strategy can the data scientist use to handle the 'payment_type' variable?

You are building a predictive model using a dataset that contains numerous variables. Upon inspection, you notice a significant number of them are highly correlated. Which of the following actions is the MOST appropriate to address potential issues with irrelevant or redundant variables before proceeding with model building?