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OMIS 482, Exam 1 questions n correct answers
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OMIS 482, Exam 1 questions n correct answers
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OMIS 482, Exam 1Append - correct answer ✔✔-Combines data sets created by different paths of a process flow in a
project
-It stacks data instead of side-by-side merging variables
• For example, combining Region A and Region B data that has same variables
Data Partition - correct answer ✔✔-Partitions data into training, validation & test data
-Training-Used to develop the model
-Validation-Used to evaluate different models created w/ training data and select the best one
-Test- Used to independently assess the selected model
File Import - correct answer ✔✔-Enables you to create a data source directly from an external file such
as Excel
-Can connect to any node
Filter - correct answer ✔✔-Can be used to eliminate observations with extreme values (outliers) in the
variables
-Not good to routinely use this-want to find out reason for outliers first
-This can follow or precede data partition node
Input Data - correct answer ✔✔-First Node in any diagram
-Specifies the data set you want to use in the diagram
Merge - correct answer ✔✔-Used to combine different data sets within a project
-Can combine output data sets from different nodes
-Combines data as side-by-side merging
, StatExplore - correct answer ✔✔-Chi-square stat shows the strength of the relationship between the
target & each categorical input variable.
-Can create chi-square stats for continuous variables, but you have to create categorical variables from
them first. You have to set interval variables to yes and specify number of bins.
-Shows a chi-square plot and variable worth plot when run.
-Variable worth-calculated from the p-values of the chi-square stats
-Variables with the highest chi-square are the most important
StatExplore for continuous targets - correct answer ✔✔-Interval Variables property set to No and set
correlations, Pearson correlations and spearman correlations to yes.
Multiplot Node - correct answer ✔✔-Shows plots of all inputs and input levels (if categorical) against the
target (frequency plots)
Graph Explore Node - correct answer ✔✔Select variables you want to plot and their roles
Variable Clustering Node: - correct answer ✔✔-Divides the inputs in a predictive modeling data set into
disjoint clusters or groups
-Disjoint- If in one cluster, cannot appear in any other cluster
-Inputs in a cluster are strongly inter-correlated and the inputs included in any other cluster are NOT
strongly correlated with the inputs in any other cluster
-You can then estimate a predictive model by including only 1 variable from each cluster or a linear
combination of all variables in that cluster. This reduces the severity of collinearity and results in having
fewer variables to deal with in the model.
-Starts with all variables in one cluster and divides it into smaller and smaller clusters using an algorithm
-Automatically excludes variables with the role set to target
Variable Selection Node: - correct answer ✔✔-Used for variable selection
-Can look at the strength of the relationship of input variables with target variable by using chi-square or
r-squared values.
-Interval targets- R-square
-Binary targets-Both r-square and chi-square
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