Feature engineering suggestion required The 2019 Stack Overflow Developer Survey Results Are...

What is the most effective way of iterating a std::vector and why?

What are the motivations for publishing new editions of an existing textbook, beyond new discoveries in a field?

Can a flute soloist sit?

Am I thawing this London Broil safely?

Aging parents with no investments

How come people say “Would of”?

What is the accessibility of a package's `Private` context variables?

Resizing object distorts it (Illustrator CC 2018)

What is the closest word meaning "respect for time / mindful"

Did Section 31 appear in Star Trek: The Next Generation?

Why hard-Brexiteers don't insist on a hard border to prevent illegal immigration after Brexit?

Did 3000BC Egyptians use meteoric iron weapons?

Geography at the pixel level

What to do when moving next to a bird sanctuary with a loosely-domesticated cat?

What do hard-Brexiteers want with respect to the Irish border?

Why isn't airport relocation done gradually?

Is flight data recorder erased after every flight?

Apparent duplicates between Haynes service instructions and MOT

Time travel alters history but people keep saying nothing's changed

What is the motivation for a law requiring 2 parties to consent for recording a conversation

What is the meaning of Triage in Cybersec world?

What is the meaning of the verb "bear" in this context?

Lightning Grid - Columns and Rows?

Origin of "cooter" meaning "vagina"



Feature engineering suggestion required



The 2019 Stack Overflow Developer Survey Results Are InFeature Extraction Technique - Summarizing a Sequence of DataPrepping Data For Usage ClusteringGround-truth and feature extraction for predictive modellingHow to use neural network's hidden layer output for feature engineering?Fix missing data by adding another feature instead of using the mean?What are best practices for collaborative feature engineering?How would knowing spammers email address improve spam detection algorithms?Is this a good practice of feature engineering?How do I develop a system to Recommend a marketing channel using data science?Cant get LSTM model to give required predictions












4












$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$








  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago
















4












$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$








  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago














4












4








4





$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$




I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"







machine-learning feature-engineering data-science-model






share|improve this question









New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question









New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|improve this question




share|improve this question








edited 1 hour ago







SSuram













New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









asked 2 hours ago









SSuramSSuram

214




214




New contributor




SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.





New contributor





SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






SSuram is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.








  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago














  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago








1




1




$begingroup$
could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
$endgroup$
– Pedro Henrique Monforte
2 hours ago




$begingroup$
could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
$endgroup$
– Pedro Henrique Monforte
2 hours ago












$begingroup$
I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
$endgroup$
– SSuram
1 hour ago




$begingroup$
I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
$endgroup$
– SSuram
1 hour ago












$begingroup$
Yes, just add it to your question and it will be perfect
$endgroup$
– Pedro Henrique Monforte
1 hour ago




$begingroup$
Yes, just add it to your question and it will be perfect
$endgroup$
– Pedro Henrique Monforte
1 hour ago










1 Answer
1






active

oldest

votes


















2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago












Your Answer





StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});






SSuram is a new contributor. Be nice, and check out our Code of Conduct.










draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49088%2ffeature-engineering-suggestion-required%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago
















2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago














2












2








2





$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$



Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.







share|improve this answer














share|improve this answer



share|improve this answer








edited 44 mins ago


























community wiki





2 revs
Pedro Henrique Monforte













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago


















  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago
















$begingroup$
I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
$endgroup$
– SSuram
1 hour ago




$begingroup$
I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
$endgroup$
– SSuram
1 hour ago












$begingroup$
What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
$endgroup$
– SSuram
53 mins ago






$begingroup$
What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
$endgroup$
– SSuram
53 mins ago














$begingroup$
that will actually return zero for near every case
$endgroup$
– Pedro Henrique Monforte
42 mins ago




$begingroup$
that will actually return zero for near every case
$endgroup$
– Pedro Henrique Monforte
42 mins ago










SSuram is a new contributor. Be nice, and check out our Code of Conduct.










draft saved

draft discarded


















SSuram is a new contributor. Be nice, and check out our Code of Conduct.













SSuram is a new contributor. Be nice, and check out our Code of Conduct.












SSuram is a new contributor. Be nice, and check out our Code of Conduct.
















Thanks for contributing an answer to Data Science Stack Exchange!


  • Please be sure to answer the question. Provide details and share your research!

But avoid



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


Use MathJax to format equations. MathJax reference.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49088%2ffeature-engineering-suggestion-required%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







Popular posts from this blog

“%fieldName is a required field.”, in Magento2 REST API Call for GET Method Type The Next...

How to change City field to a dropdown in Checkout step Magento 2Magento 2 : How to change UI field(s)...

變成蝙蝠會怎樣? 參考資料 外部連結 导航菜单Thomas Nagel, "What is it like to be a...