Lift

 
Like its name indicates a lift is a measure on how good your binary classifier model is lifting the predictions. In other words it measures how well the new models choice is better than an old models choice or the random selection. A lift plot is an alternative to a ROC curve when you want to compare two classifier, it provides insights on the model and can help to determine a cutoff.

Lets start with the definition of list:
The lift measures the change in concentration of a target value if applied to a subgroup of the test set that is chosen by the model. Note that the lift is always connected to the concentration of the target value in the test set, the lower the concentration, the higher is also the possible lift of the model. Therefore there are no restrictions to the values list of the lift.

In an example:
Imagine that you want to improve a marketing campaign which adresses customers in order to sell new products or services. From the past campaign you build up a predictive model trying to identify the customers who are likely to respond. In the past campaign 4% of the overall adressed customer responded. If you now choose a random subset of 10 % of the adressed customers, you would expect 4% of the responders in there.
Now with your new model you can identify possible responders and you therefore choose the 10% most likely to respond to the campaign. If from these 4% of all customers 16% respond, then your classifier has a lift of 16 / 4 = 6.
Now you can calculate the expected responses adressing 20%, 30%, ... and you can plot the data in a so called lift chart, that could look like this (the yellow line corresponds to the random classifier, the red one to the fictious model):

These lift charts can be built up for different classifiers in order to compare them. Also from the form of the curve a possible maximum of adressed customer can be choosen in order to optimize the costs for the campaigns.