Vertica Analytics Platform Version 9.2.x Documentation
ROC
Returns a table that displays the points on a receiver operating characteristic curve. The ROC
function tells you the accuracy of a classification model as you raise the discrimination threshold for the model.
Syntax
ROC ( targets, probabilities [ USING PARAMETERS [num_bins=num‑bins] [, AUC=output] [, main_class=class‑name ] ) ] ) OVER()
Arguments
targets 
An input column that contains the true values of the response variable, one of the following data types: INTEGER, BOOLEAN, or CHAR/VARCHAR. Depending on the column data type, the function processes column data as follows:
If the input column is of data type INTEGER or BOOLEAN, the function ignores parameter 
probabilities 
A FLOAT input column that contains the predicted probability of response being the main class, set to 1 if targets is of type INTEGER. 
Parameter Settings
Parameter name  Set to… 

num_bins

An integer value that determines the number of decision boundaries. Decision boundaries are set at equally spaced intervals between 0 and 1, inclusive. The function computes the table at each num‑bin + 1 point. Default: 100 
AUC

A Boolean value that specifies whether to output the area under the curve (AUC) value. Default: True 
main_class

Used only if target is of type CHAR/VARCHAR, specifies the class to associate with the probabilities argument. 
Examples
Execute ROC
on input table mtcars
. Observed class labels are in column obs
, predicted class labels are in column prob
:
=> SELECT ROC(obs::int, prob::float USING PARAMETERS num_bins=5, AUC = True) OVER() FROM (SELECT am AS obs, PREDICT_LOGISTIC_REG (mpg, cyl, disp, drat, wt, qsec, vs, gear, carb USING PARAMETERS model_name='myLogisticRegModel', type='probability') AS prob FROM mtcars) AS prediction_output;
decision_boundary  false_positive_rate  true_positive_rate  AUC comment ++++ 0  1  1   0.5  0  1   1  0  0  1  Of 32 rows,32 were used and 0 were ignoreded (3 rows)
The function returns a table with the following results:
decision_boundary
indicates the cutoff point for whether to classify a response as 0 or 1. In each row, ifprob
is equal to or greater thandecision_boundary
, the response is classified as 1. Ifprob
is less thandecision_boundary
, the response is classified as 0.false_positive_rate
shows the percentage of false positives (when 0 is classified as 1) in the correspondingdecision_boundary
.true_positive_rate
shows the percentage of rows that were classified as 1 and also belong to class 1.
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