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**R Data Science:**
R Programming A-Z: R For Data Science With Real Exercises!

This tutorial demonstrates how to calculate gain and lift chart with R. Gain and Lift charts are used to measure the performance of a predictive classification model. They measure how much better results one can expect with the predictive classification model comparing without a model. The lift metrics is a very popular metrics to evaluate the customer and marketing analytics model. The 'lift' term comes from marketing analytics domain.

You can export gain table in CSV format by submitting the following code -

**R Function : Gain and Lift Chart**lift <- function(depvar, predcol, groups=10) {

if(!require(dplyr)){

install.packages("dplyr")

library(dplyr)}

if(is.factor(depvar)) depvar <- as.integer(as.character(depvar))

if(is.factor(predcol)) predcol <- as.integer(as.character(predcol))

helper = data.frame(cbind(depvar, predcol))

helper[,"bucket"] = ntile(-helper[,"predcol"], groups)

gaintable = helper %>% group_by(bucket) %>%

summarise_at(vars(depvar), funs(total = n(),

totalresp=sum(., na.rm = TRUE))) %>%

mutate(Cumresp = cumsum(totalresp),

Gain=Cumresp/sum(totalresp)*100,

Cumlift=Gain/(bucket*(100/groups)))

return(gaintable)

}

**Run Function**

dt = lift(churn$target , churn$prediction, groups = 10)

In the

**first parameter**of the lift function, you need to define a column wherein binary target variable (dependent variable) is stored. In this case, churn$target : churn is the data frame and target is the dependent variable column.
In the

**second parameter**, you need to specify the predicted probability variable.**How to plot Cumulative Lift**

Run the following code to create lift chart.

graphics::plot(dt$bucket, dt$Cumlift, type="l", ylab="Cumulative lift", xlab="Bucket")

Lift Chart |

**Interpretation of Cumulative Lift**The Cumulative Lift of 3.4 for top two deciles, means that when selecting 20% of the records based on the model, one can expect 3.4 times the total number of targets (events) found by randomly selecting 20%-of-records without a model. In terms of customer attrition (churn) model, we can say we can cover 3.4 times the number of attritors by selecting only 20% of the customers based on the model as compared to 20% customer selection randomly.

**Interpretation of Gain**

% of targets (events) covered at a given decile level. For example, 67.9% of events covered in top 20% of data based on model.

**Export gain and lift table**

You can export gain table in CSV format by submitting the following code -

# Export gain table

write.csv(dt, "D:\\lift.csv", row.names = FALSE)

**Related Tutorial - Understand Gain and Lift Chart**

excelente

ReplyDeleteThanks for the code. However, after running it, the gain table that I am getting, its not dividing the data into 10 equal groups, each group is having different observation number, ideally which should not be the case. Need help on this.

ReplyDeleteIt is because some of predicted probability scores are equal so one or two groups are not of same size. I am assuming the difference of number of observations in each group is very minimal. For example, c(0.1,0.2,0.3,0.3,0.4,0.5) - you cannot assign different rank to 0.3 in this vector.

DeleteTo have same observations in each group, you can add ties.method = "first" after groups = 10 in the code. Revised code would be -

gaint = gains(actual= valgain[,1], predicted=pred ,groups=10, ties.method = "first")

Hope it helps!