Here you have the best SAS Institue A00-240 practice exam questions
When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?
The sample means from the training data set are applied to the validation and test data sets. This is to ensure that the imputation strategy appropriately reflects the training data, which is the basis for model learning. Applying the mean imputation from the training set to the validation and test sets avoids data leakage and ensures a fair assessment of the model performance on unseen data.
This question will ask you to provide missing code segments.
A logistic regression model was fit on a data set where 40% of the outcomes were events (TARGET=1) and 60% were non-events (TARGET=0). The analyst knows that the population where the model will be deployed has 5% events and 95% non-events. The analyst also knows that the company's profit margin for correctly targeted events is nine times higher than the company's loss for incorrectly targeted non-event.
Given the following SAS program:
What X and Y values should be added to the program to correctly score the data?
The logistic regression model's priorevent parameter (X) should be set to the proportion of events in the population where the model will be deployed, which is 0.05. For the Y value, to correctly target events considering the company's profit margin, you need to set a cut-off probability for deciding if an observation should be classified as an event (TARGET=1). The ratio of the profit margin for correctly targeted events to the cost of incorrectly targeted non-events suggests a threshold calculation. Since the logistic model was originally fit on a population with 40% events, Y should be set to 0.40 to match this ratio. Thus, X should be 0.05 and Y should be 0.40.
An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process.
What is the purpose of the training data set?
The purpose of the training data set is to build the predictive models. The training data is used by the machine learning algorithm to learn patterns and make predictions. It forms the basis from which the model learns and optimizes its parameters.
There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?
When dealing with missing values in the input variables for a regression application, the SAS procedure that provides a viable solution is STDIZE (not STDI2E as there seems to be a typo). The STDIZE procedure is used to standardize variables, and it can handle missing values by computing statistics that exclude those missing values.
Refer to the following exhibit:
What is a correct interpretation of this graph?
The graph displays the empirical logit, which relates to the log-odds of a binary response variable, plotted against a continuous predictor variable (x). The graph shows a quadratic curve fit through these points, indicating that the relationship between the continuous predictor (x) and the log-odds of the binary response is quadratic.