E-Miner Quiz

What is data preprocessing in SAS Enterprise Miner?

• Data preprocessing in SAS Enterprise Miner refers to the steps taken to prepare and clean the data before analysis.

• Data preprocessing in SAS Enterprise Miner refers to the steps taken to interpret the data.

• Data preprocessing in SAS Enterprise Miner refers to the steps taken to visualize the data.

• Data preprocessing in SAS Enterprise Miner refers to the steps taken to analyze the data.

In market basket analysis, which of the following statistics represents the proportion of customers who bought the left and right side products of a Market Basket Rule

• Lift

• Confidence

• Support

• Gini Coefficient

What are some common techniques used for data preprocessing in SAS Enterprise Miner?

• data sampling

• data cleaning, data transformation, data reduction, and data discretization

• data imputation

• data normalization

Default sample size in SAS Enterprise Miner is?

• 10000

• 2000

• 1500

• Max

How does variable selection help in model building in SAS Enterprise Miner?

• Variable selection helps in identifying the most relevant and influential variables for predicting the target variable.

• Variable selection helps in reducing the accuracy of the model.

• Variable selection is not necessary for model building in SAS Enterprise Miner.

• Variable selection only considers irrelevant variables for predicting the target variable.

What are some popular variable selection methods used in SAS Enterprise Miner?

• random selection

• top-down selection

• bottom-up selection

• stepwise selection, backward elimination, forward selection

In SAS Enterprise Miner, Tools within the sample tab are used for changing the number of?

• Rows

• Columns

• Both

• missing values

What is the advantage of decision tree over regression modelling?

• It can handle missing values

• It gives more accurate results

• It is easy to use

• We can enhance the required model performance by choosing the maximal tree.

To handle the extreme or unusual values, which of the following tool can be used?

• Transform

• Impute

• Replacement

• Variable Selection

If the target variable is binary and the purpose is estimation of probabilities, then the most appropriate assessment method is:

• Average square Error

• Misclassification rate

• accuracy

• Coconcordance