LIFT_TABLE
Returns a table that compares the predictive quality of a machine learning model. This function is also known as a lift chart.
Syntax
LIFT_TABLE ( targets, probabilities [ USING PARAMETERS [num_bins=num‑bins] [, 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 
main_class

Used only if target is of type CHAR/VARCHAR, specifies the class to associate with the probabilities argument. 
Examples
Execute LIFT_TABLE
on an input table mtcars
.
=> SELECT LIFT_TABLE(obs::int, prob::float USING PARAMETERS num_bins=2) 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  positive_prediction_ratio  lift  comment +++ 1  0  NaN  0.5  0.40625  2.46153846153846  0  1  1  Of 32 rows, 32 were used and 0 were ignored (3 rows)
The first column, decision_boundary
, indicates the cutoff point for whether to classify a response as 0 or 1. For instance, for each row, if prob
is greater than or equal to decision_boundary
, the response is classified as 1. If prob
is less than decision_boundary
, the response is classified as 0.
The second column, positive_prediction_ratio
, shows the percentage of samples in class 1 that the function classified correctly using the corresponding decision_boundary
value.
For the third column, lift
, the function divides the positive_prediction_ratio
by the percentage of rows correctly or incorrectly classified as class 1.