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provide graphical evaluations of a set of classification models for the loans data set do not include interest as a
1 prepare and interpret a response chart comparing four two models2 construct and interpret separate roi charts for the
construct and interpret separate profits charts for each of the four models extra credit find a way to construct a
1 to which cluster for the 90210 zip code would you prefer to belong2 describe the goal of all clustering methods3 what
1 using all of the variables except name and rating run the k-means algorithm with k5 to identify clusters within the
1 should the analyst always choose the cluster solution with the better mean silhouette value explain2 explain how the
1 how is average silhouette interpreted2 when will a data value have a perfect silhouette value what is this value3
1 what is a silhouette what is its range is it a characteristic of a cluster a variable or a data value2 how do we
1 why do we need evaluation measures for cluster algorithms2 what is cluster separation and cluster cohesion3 why is
1 calculate model cost for each of the five different sortings which model has the highest profitability or the lowest
using the loans data set demonstrate that it is bad practice to include interest with the other predictors as follows
1 is the ms value always indicative of the best cluster solution2 run birch on each of the five different sortings
1 describe the parameters of the cf tree2 why is phase 2 of the birch algorithm efficient3 why is it bad practice to
1 why is birch appropriate for streaming data2 describe the two phases of the birch clustering algorithm3 what is a cf4
1 using the information above and any other information you can bring to bear construct detailed and informative
1 if your software supports this construct a web graph of income marital status and the other categorical variables
1 construct a bar chart of the cluster membership with an overlay of income discuss your findings compare to the
1 using weights and distance explain clearly why a certain output node will win the competition for the input of a
set the minimum antecedent support to 1 the minimum rule confidence to 5 and the maximum number of antecedents to 1a
1 describe the two main methods of representing market basket data what are the benefits and drawbacks of each2
1 compare the mean silhouette values for the two cluster models which model is preferred2 compare the pseudo-f
repeat exercises 18-22 using k-means with k 4exercise 18use k-means with k 3 to generate a cluster model with the
using the same variables as the previous exercise provide a two-dimensional scatter plot with an overlay of binned
1 use k-means with k 3 to generate a cluster model with the training data set2 generate a silhouette plot of your