What is the correct way to compute lift in lift charts
$begingroup$
How is "lift" computed? i was reading about "Gain and lift charts" in data science.
I picked the following example 
from https://www.listendata.com/2014/08/excel-template-gain-and-lift-charts.html
I am clear on how the gain values are computed. Not clear about lift values are computed? (last column in table)
machine-learning metric
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
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bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
How is "lift" computed? i was reading about "Gain and lift charts" in data science.
I picked the following example from https://www.listendata.com/2014/08/excel-template-gain-and-lift-charts.html
I am clear on how the gain values are computed. Not clear about lift values are computed? (last column in table)
machine-learning metric
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
$endgroup$
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
How is "lift" computed? i was reading about "Gain and lift charts" in data science.
I picked the following example from https://www.listendata.com/2014/08/excel-template-gain-and-lift-charts.html
I am clear on how the gain values are computed. Not clear about lift values are computed? (last column in table)
machine-learning metric
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
$endgroup$
How is "lift" computed? i was reading about "Gain and lift charts" in data science.
I picked the following example from https://www.listendata.com/2014/08/excel-template-gain-and-lift-charts.html
I am clear on how the gain values are computed. Not clear about lift values are computed? (last column in table)
machine-learning metric
machine-learning metric
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
asked Jul 27 '18 at 10:34
Anuj GuptaAnuj Gupta
1667
1667
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 4 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
Lift is computed by comparing performance with a random selection model. I'll explain with your example below,
- assume that we didn't have any statistical/ML model for ranking/scoring the respondents.
- In that case assume we did a random ordering of respondents.
- A decile (10% of total population) is expected to have 10% of the respondents. In your case, there should've been (approximately) 488 respondents in 2500 cases.
- But after ordering the cases by score, you are seeing 44.71% of the cases in first decile against expected 10% (in random/no model case). This gives the gain of 44.71/10 = 4.471.
- For next decile, cumulatively you have covered 20% of the cases. You'd expect a random/no model scenario covers 20% of the respondents. But using scores, we covered 80% of them. That gives a cumulative lift of 80/20 = 4.
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
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1 Answer
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1 Answer
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$begingroup$
Lift is computed by comparing performance with a random selection model. I'll explain with your example below,
- assume that we didn't have any statistical/ML model for ranking/scoring the respondents.
- In that case assume we did a random ordering of respondents.
- A decile (10% of total population) is expected to have 10% of the respondents. In your case, there should've been (approximately) 488 respondents in 2500 cases.
- But after ordering the cases by score, you are seeing 44.71% of the cases in first decile against expected 10% (in random/no model case). This gives the gain of 44.71/10 = 4.471.
- For next decile, cumulatively you have covered 20% of the cases. You'd expect a random/no model scenario covers 20% of the respondents. But using scores, we covered 80% of them. That gives a cumulative lift of 80/20 = 4.
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
$endgroup$
add a comment |
$begingroup$
Lift is computed by comparing performance with a random selection model. I'll explain with your example below,
- assume that we didn't have any statistical/ML model for ranking/scoring the respondents.
- In that case assume we did a random ordering of respondents.
- A decile (10% of total population) is expected to have 10% of the respondents. In your case, there should've been (approximately) 488 respondents in 2500 cases.
- But after ordering the cases by score, you are seeing 44.71% of the cases in first decile against expected 10% (in random/no model case). This gives the gain of 44.71/10 = 4.471.
- For next decile, cumulatively you have covered 20% of the cases. You'd expect a random/no model scenario covers 20% of the respondents. But using scores, we covered 80% of them. That gives a cumulative lift of 80/20 = 4.
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
$endgroup$
add a comment |
$begingroup$
Lift is computed by comparing performance with a random selection model. I'll explain with your example below,
- assume that we didn't have any statistical/ML model for ranking/scoring the respondents.
- In that case assume we did a random ordering of respondents.
- A decile (10% of total population) is expected to have 10% of the respondents. In your case, there should've been (approximately) 488 respondents in 2500 cases.
- But after ordering the cases by score, you are seeing 44.71% of the cases in first decile against expected 10% (in random/no model case). This gives the gain of 44.71/10 = 4.471.
- For next decile, cumulatively you have covered 20% of the cases. You'd expect a random/no model scenario covers 20% of the respondents. But using scores, we covered 80% of them. That gives a cumulative lift of 80/20 = 4.
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
$endgroup$
Lift is computed by comparing performance with a random selection model. I'll explain with your example below,
- assume that we didn't have any statistical/ML model for ranking/scoring the respondents.
- In that case assume we did a random ordering of respondents.
- A decile (10% of total population) is expected to have 10% of the respondents. In your case, there should've been (approximately) 488 respondents in 2500 cases.
- But after ordering the cases by score, you are seeing 44.71% of the cases in first decile against expected 10% (in random/no model case). This gives the gain of 44.71/10 = 4.471.
- For next decile, cumulatively you have covered 20% of the cases. You'd expect a random/no model scenario covers 20% of the respondents. But using scores, we covered 80% of them. That gives a cumulative lift of 80/20 = 4.
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
answered Jul 30 '18 at 9:16
hssayhssay
1,0931311
1,0931311
add a comment |
add a comment |
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