ran as first) to the variable which contributes the most to minimizing the loss function. Due to the small number of predictors in this test, a simple tree rather than a more complicated Random Forest model was used. The rpart function from R, which is a very common function from R used for tree modeling, was used. The statistics were examines to determine which K (i.e., how many clusters) seemed to perform the best separation of records and which clustering method (RF clustering or Euclidean clustering) performed best. The following shows the chi square statistics: Table 6: Chi Square Statistic for Random Forest and Euclidean Clusters Random K Forest Euclidean 2 183 254 3 604 191 4 715 254 The Chi Square statistic suggests that the Random Forest clusters for k= 4 groups does the best job of segmenting claims into groups based on the probability of suspicious claims. The table of the proportion of suspicious claims with each cluster is shown for the Random Forest and Euclidean clusters with k=4 groups: Figure 5: Tree of Suspicion of Questionable Claims and Cluster Table 7: Proportion of Suspicious Claims by Cluster Random Forest Euclidean Group % Suspicious N % Suspicious N 1 24% 501 54% 536 2 2% 389 5% 365 3 91% 317 26% 390 4 17% 293 25% 209 The statistics for the Random forest clusters indicate that a high suspicion cluster has 91 percent of claims that are suspicious and a low suspicion cluster has 2% of claims that are suspicious. Two random forest clusters have mean somewhat modest mean percentages (17% and 24%). This compares to the Euclidean clustering where the high suspicion cluster has 54% suspicious claims and the low suspicion cluster has 5% suspicious claims and two moderate clusters have about 25% suspicious claims.
ran as first) to the variable which contributes the most to minimizing the loss function. Due to the small number of predictors in this test, a simple tree rather than a more complicated Random Forest model was used. The rpart function from R, which is a very common function from R used for tree modeling, was used. The statistics were examines to determine which K (i.e., how many clusters) seemed to perform the best separation of records and which clustering method (RF clustering or Euclidean clustering) performed best. The following shows the chi square statistics: Table 6: Chi Square Statistic for Random Forest and Euclidean Clusters K Random Forest Euclidean 2 183 254 3 604 191 4 715 254 The Chi Square statistic suggests that the Random Forest clusters for k= 4 groups does the best job of segmenting claims into groups based on the probability of suspicious claims. The table of the proportion of suspicious claims with each cluster is shown for the Random Forest and Euclidean clusters with k=4 groups: Figure 5: Tree of Suspicion of Questionable Claims and Cluster Table 7: Proportion of Suspicious Claims by Cluster Random Forest Euclidean Group % Suspicious N % Suspicious N 1 24% 501 54% 536 2 2% 389 5% 365 3 91% 317 26% 390 4 17% 293 25% 209 The statistics for the Random forest clusters indicate that a high suspicion cluster has 91 percent of claims that are suspicious and a low suspicion cluster has 2% of claims that are suspicious. Two random forest clusters have mean somewhat modest mean percentages (17% and 24%). This compares to the Euclidean clustering where the high suspicion cluster has 54% suspicious claims and the low suspicion cluster has 5% suspicious claims and two moderate clusters have about 25% suspicious claims.
Two%20Unsupervised%20Learning%20Techniques%20v3.1.docx