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You have developed a predictive model using a very large set of independent variables. Despite achieving outstanding performance on your training dataset, when you apply your model to new, unseen data, the performance significantly deteriorates. Which of the following is the most likely explanation for this occurrence?

You are applying PCA on a new dataset as part of a dimensionality reduction effort. Given an eigenvalue decomposition of the covariance matrix, what criterion is often used to decide how many principal components to retain?

You have built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?

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?

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?