Free SAS Institute A00-225 Exam Questions

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?

As a data scientist, you have built three predictive models to forecast the risk of a rare event occurring within a patient group. To evaluate these models, you have computed several fit statistics. Consider the following statistics for the models: Model A: - BIC: 182 - AIC: 175 - KS: 0.65 - Brier Score: 0.12 Model B: - BIC: 185 - AIC: 178 - KS: 0.60 Brier Score: 0.11 Model C: - BIC: 180 - AIC: 182 - KS: 0.80 - Brier Score: 0.09 Assuming that the most important criteria for model selection is the prediction accuracy of the rare event and considering the disease is highly imbalanced, which model should you recommend?

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 are building an artificial neural network model to predict the probability that a customer will respond to a marketing campaign. The target variable is binary, indicating a '1' for a response and '0' for no response. Which combination of activation function and error function would be most appropriate for the output layer of this neural network?

A data analyst is using the RANDOMFOREST procedure to create a predictive model. They want to specify the number of trees to be generated in the random forest for a robust prediction. Which option in the RANDOMFOREST statement correctly sets the desired number of trees?