In this part of the tutorial you will learn more about definition and types of lift curves, accumulated LIFT with percentage scale how to construct a LIFT curve. You will gain more information about the accumulated LIFT with percentage scale and other types of LIFT curves.
In the previous parts of our tutorial we discussed:
In this last part of the tutorial we will discuss the LIFT curve.
A lift chart pictures gains from applying a classification model in comparison to not applying it (i.e. using a random classifier) for a given section of data.
Two simple examples are shown below.
4 types of LIFT curves can be considered.
LIFT with percentage scale
LIFT with quotient scale
In order to construct a LIFT curve one needs to sort all the observation according to the decreasing scores generated by our model. In the example below we sort the values in the second column.
We determine the number of quantiles, into which our observations will be divided. For instance, when dividing into quartiles, i.e. quantiles of orders ¼, ½, ¾ we get:
The number of quantiles is significant:
Next we display the concentration of positive observations (e.g. churn) in each quantile.
General description
Specific example – sending new offer to customers
General description
Specific example – sending new offer to customers
Perfect classifiers
General description
Specific example – sending new offer to customers
General description
Specific example: sending new offer to customers