# Lift Curve¶

Measures the performance of a chosen classifier against a random classifier.

## Description¶

The **Lift curve** shows the relation between the number of instances which
were predicted positive and those that are indeed positive and
thus measures the performance of a chosen classifier against a random
classifier. The graph is constructed with the cumulative number of cases
(in descending order of probability) on the x-axis and the cumulative
number of true positives on the y-axis. Lift curve is often used in
segmenting the population, e.g., plotting the number of responding
customers against the number of all customers contacted. You can also
determine the optimal classifier and its threshold from the graph.

- Choose the desired
*Target class*. The default class is chosen alphabetically. - If test results contain more than one classifier, the user can choose which curves she or he wants to see plotted. Click on a classifier to select or deselect the curve.
*Show lift convex hull*plots a convex hull over lift curves for all classifiers (yellow curve). The curve shows the optimal classifier (or combination thereof) for each desired TP/P rate.- Press
*Save Image*if you want to save the created image to your computer in a .svg or .png format. - Produce a report.
- 2-D pane with
**P rate**(population) as x-axis and**TP rate**(true positives) as a y-axis. The diagonal line represents the behaviour of a random classifier. Click and drag to move the pane and scroll in or out to zoom. Click on the “*A*” sign at the bottom left corner to realign the pane.

Note

The perfect classifier would have a steep slope towards 1 until all classes are guessed correctly and then run straight along 1 on y-axis to (1,1).

## Example¶

At the moment, the only widget which gives the right type of the signal
needed by the **Lift Curve** is Test&Score.

In the example below, we try to see the prediction quality for the class
‘survived’ on the *Titanic* data set. We compared three different
classifiers in the Test Learners widget and sent them to Lift Curve to see
their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns
with *lift convex hull*. We also see that its performance is the best
for the first 30% of the population (in order of descending
probability), which we can set as the threshold for optimal
classification.