hanaml.MulticlassAuc {hana.ml.r} | R Documentation |
hanaml.MulticlassAuc is a R wrapper for PAL multi-class Auc.
hanaml.MulticlassAuc(conn.context, data.original, data.predict, key = NULL)
conn.context |
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data.original |
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data.predict |
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key |
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R6Class
object.
Return a "MulticlassAuc" object with following values:
auc : double
The area under the receiver operating characteristic curve.
roc : DataFrame
False positive rate and true positive rate, structured as follows:
ID, INTEGER
- column with index
FPR, DOUBLE
-
representing false positive rate.
TPR, DOUBLE
-
representing true positive rate.
For each data point ID, there should be one row for each possible class.
## Not run: Input DataFrame data.original and data.predict: > data.original$Collect() ID ORIGINAL 0 1 1 1 2 1 2 3 1 3 4 2 4 5 2 5 6 2 6 7 3 7 8 3 8 9 3 9 10 3 > data.predict$Collect() ID PREDICT PROB 0 1 1 0.90 1 1 2 0.05 2 1 3 0.05 3 2 1 0.80 4 2 2 0.05 5 2 3 0.15 6 3 1 0.80 7 3 2 0.10 8 3 3 0.10 9 4 1 0.10 10 4 2 0.80 11 4 3 0.10 12 5 1 0.20 13 5 2 0.70 14 5 3 0.10 15 6 1 0.05 16 6 2 0.90 17 6 3 0.05 18 7 1 0.10 19 7 2 0.10 20 7 3 0.80 21 8 1 0.00 22 8 2 0.00 23 8 3 1.00 24 9 1 0.20 25 9 2 0.10 26 9 3 0.70 27 10 1 0.20 28 10 2 0.20 29 10 3 0.60 Compute Area Under Curve for multi class: > multiauc_fit <- hanaml.MulticlassAuc(conn.context = conn, data.original = data.original, data.predict = data.predict) Output: >multiauc_fit$auc 1 >multiauc_fit$roc$Collect() ID FPR TPR 0 0 1.00 1.0 1 1 0.90 1.0 2 2 0.65 1.0 3 3 0.25 1.0 4 4 0.20 1.0 5 5 0.00 1.0 6 6 0.00 0.9 7 7 0.00 0.7 8 8 0.00 0.3 9 9 0.00 0.1 10 10 0.00 0.0 ## End(Not run)