Free Online SAS Institute A00-225 Practice Test

You are working on a predictive model and you notice that your categorical variable "Color" has 50 different levels, which is adding complexity to your model. You decide to group these levels into fewer categories based on their frequency of occurrence to improve the model’s interpretability and performance. What technique would you most likely use for this task?

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 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?

When building an ARCH model, an analyst notices that large errors are followed by large errors and small errors by small errors, but the sign of the errors does not matter. This phenomenon is indicative of what type of volatility clustering?

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?