Use the Gain and Lift charts to assess the performance of your
classification model. The Gain chart plots the total positive rate in percent
versus the percent of total counts. So, for example, these charts can show that
80% of the events are in 20% of the data. Thus, by focusing on 20% of the data,
we can be efficient with our resources. The Lift chart plots the cumulative
lift (or non-cumulative lift) versus the percent of total counts.

Interpretation of Gain chart

The training and test lines represent the expected response using the
predictive model. The training data set fits the model and the test data set
evaluates the model. The dotted reference line represents a line with slope =
1, which is the expected random response without the model. Gains larger than 1
indicate that the results from the predictive model are better than random.

Interpretation of Lift chart

The training and test lines represent the expected response using the
predictive model. The training data set fits the model and the test data set
evaluates the model. Lift is the ratio of the gain percentage relative to the
expected random result. The dotted reference line represents a cumulative lift
of 1, which means that there is no gain compared with random.

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