{ "id": 9694, "title": " predict a probability that the customer repeat-purchased the product ", "url": "/competitions/acquire-valued-shoppers-challenge/discussion/9694", "author_name": "", "post_date": "2014-07-09T09:39:28.060Z", "votes": null, "comment_count": 10, "views": 4571, "content": "

In the evaluation link, it is written

\n

"For each customer (id) in testHistory.csv, predict a probability that the customer repeat-purchased the product from the promotion they received."

\n\n\n

So we have to answer in terms of only 0 or 1 as the results would be compared with the actual outcomes (which will be in form of yes or no)? Or the probability for each offer can vary in the range from 0 to 1?

", "messages": [ { "id": "50185", "postDate": "07/09/2014 09:39:28", "content": "

In the evaluation link, it is written

\n

"For each customer (id) in testHistory.csv, predict a probability that the customer repeat-purchased the product from the promotion they received."

\n\n\n

So we have to answer in terms of only 0 or 1 as the results would be compared with the actual outcomes (which will be in form of yes or no)? Or the probability for each offer can vary in the range from 0 to 1?

", "rawMarkdown": "", "votes": null }, { "id": "50239", "postDate": "07/10/2014 05:08:05", "content": "

On Evaluation link it is written,

\n

Submissions are evaluated on area under the ROC curve between the predicted probability that a customer repeat-purchased and the observed purchase outcomes.

\n

Submission File
For each customer (id) in testHistory.csv, predict a probability that the customer repeat-purchased the product from the promotion they received. Your submission file must have a header and should look like the following:

\n

id,repeatProbability
12262064,0
12277270,0
12332190,0
...

\n\n

My question is that this repeatProbabilty can only take value 0 (customer will not purchase the product) or 1 (customer will purchase the product)? Or the probability can vary between 0 and 1 i.e. we say that the probability of customer purchasing the product is 0.5? Is this valid?

\n\n

Waiting in anticipation for the reply.

\n

Please reply...

\n

I am confused.

", "rawMarkdown": "", "votes": null }, { "id": "50241", "postDate": "07/10/2014 05:30:32", "content": "

The probability ("repeatProbability") may assume values different than 0 and 1. It depends on how you are modeling it.

\n

Take a look at http://www.kaggle.com/c/acquire-valued-shoppers-challenge/forums/t/7671/evaluation/, it may help you as well.

", "rawMarkdown": "", "votes": null }, { "id": "50243", "postDate": "07/10/2014 05:50:43", "content": "

Thank you so much fzvinicius for the reply...

", "rawMarkdown": "", "votes": null }, { "id": "50262", "postDate": "07/10/2014 10:27:41", "content": "

I don't have much idea regarding AUC. If my test sample consists of either 0 or 1 only as "repeatProbability", will the AUC give correct score for my test sample as well?

", "rawMarkdown": "", "votes": null }, { "id": "50268", "postDate": "07/10/2014 13:58:38", "content": "

The value doesn't have to be from 0 to 1. Only the ranking matters, not the actual value, for the calculation of AUC. 

", "rawMarkdown": "", "votes": null }, { "id": "50331", "postDate": "07/11/2014 04:23:23", "content": "

Sorry I didn't get it. Can you please explain further?

", "rawMarkdown": "", "votes": null }, { "id": "50364", "postDate": "07/11/2014 18:19:38", "content": "

I too was confused but then I read the following on the Dashboard -> Home->'Data' page.

\n\n

• testHistory.csv - contains the incentive offered to each customer but does not include their response (you are predicting the repeater column for each id in this file)

\n

Now since the repeater column in training history.csv contains only O and 1, it is clear that we have to predict either 0 or 1. Hope this helps.

", "rawMarkdown": "", "votes": null }, { "id": "50369", "postDate": "07/11/2014 19:35:03", "content": "

Do not submit only 1s and 0s if you want a good score.

\n

The competition metric is AUC, not raw plain accuracy.

\n

A high probability may have a prediction of 0.956213 for example and low proba 0.052311. If your model is not sure it should predict near 0.5.

\n

Also only the rank matters. If all your predictions are above 0.9 but your low-probability predictions are ranked below high-probability predictions, you will still get a good score.

\n

Look up area under curve and see the Kaggle wiki.

", "rawMarkdown": "", "votes": null }, { "id": "50386", "postDate": "07/12/2014 08:59:59", "content": "

To Triskelion,

\n

As pointed out by rashmirin, "the repeater column in trainingHistory.csv contains only 0 or 1".

\n

I was building a model considering the repeater column as the CLASS label. So my testing data when submitted to that model, would return either 0 or 1.

\n

But now as pointed out by you, we have to predict the probability which can take any value between 0 and 1.

\n

So it means that after applying some pre processing on my training data, each repeater column indicating 1 value should return a high prob value and each repeater column indicating 0 in training data should return a low prob value.

\n

I am just a beginner in data analysis. Any guidance on this would be highly appreciated.

", "rawMarkdown": "", "votes": null }, { "id": "50396", "postDate": "07/12/2014 12:49:56", "content": "

@Trisco7

\n

Not sure what you mean there but the Target / label doesn't change. It remains 0 and 1. But what Triskelion is advising is to submit probabilities of between 0 and 1. Where 1 is 100% confidence that the likelihood of the label is 1.

", "rawMarkdown": "", "votes": null } ], "comments": [ { "id": 50239, "author_name": "trisco7", "author_url": "", "post_date": "07/10/2014 05:08:05", "content": "

On Evaluation link it is written,

\n

Submissions are evaluated on area under the ROC curve between the predicted probability that a customer repeat-purchased and the observed purchase outcomes.

\n

Submission File
For each customer (id) in testHistory.csv, predict a probability that the customer repeat-purchased the product from the promotion they received. Your submission file must have a header and should look like the following:

\n

id,repeatProbability
12262064,0
12277270,0
12332190,0
...

\n\n

My question is that this repeatProbabilty can only take value 0 (customer will not purchase the product) or 1 (customer will purchase the product)? Or the probability can vary between 0 and 1 i.e. we say that the probability of customer purchasing the product is 0.5? Is this valid?

\n\n

Waiting in anticipation for the reply.

\n

Please reply...

\n

I am confused.

", "votes": null, "replies": [] }, { "id": 50241, "author_name": "fzvinicius", "author_url": "", "post_date": "07/10/2014 05:30:32", "content": "

The probability ("repeatProbability") may assume values different than 0 and 1. It depends on how you are modeling it.

\n

Take a look at http://www.kaggle.com/c/acquire-valued-shoppers-challenge/forums/t/7671/evaluation/, it may help you as well.

", "votes": null, "replies": [] }, { "id": 50243, "author_name": "trisco7", "author_url": "", "post_date": "07/10/2014 05:50:43", "content": "

Thank you so much fzvinicius for the reply...

", "votes": null, "replies": [] }, { "id": 50262, "author_name": "trisco7", "author_url": "", "post_date": "07/10/2014 10:27:41", "content": "

I don't have much idea regarding AUC. If my test sample consists of either 0 or 1 only as "repeatProbability", will the AUC give correct score for my test sample as well?

", "votes": null, "replies": [] }, { "id": 50268, "author_name": "mikeskim", "author_url": "", "post_date": "07/10/2014 13:58:38", "content": "

The value doesn't have to be from 0 to 1. Only the ranking matters, not the actual value, for the calculation of AUC. 

", "votes": null, "replies": [] }, { "id": 50331, "author_name": "trisco7", "author_url": "", "post_date": "07/11/2014 04:23:23", "content": "

Sorry I didn't get it. Can you please explain further?

", "votes": null, "replies": [] }, { "id": 50364, "author_name": "rdatascientist", "author_url": "", "post_date": "07/11/2014 18:19:38", "content": "

I too was confused but then I read the following on the Dashboard -> Home->'Data' page.

\n\n

• testHistory.csv - contains the incentive offered to each customer but does not include their response (you are predicting the repeater column for each id in this file)

\n

Now since the repeater column in training history.csv contains only O and 1, it is clear that we have to predict either 0 or 1. Hope this helps.

", "votes": null, "replies": [] }, { "id": 50369, "author_name": "triskelion", "author_url": "", "post_date": "07/11/2014 19:35:03", "content": "

Do not submit only 1s and 0s if you want a good score.

\n

The competition metric is AUC, not raw plain accuracy.

\n

A high probability may have a prediction of 0.956213 for example and low proba 0.052311. If your model is not sure it should predict near 0.5.

\n

Also only the rank matters. If all your predictions are above 0.9 but your low-probability predictions are ranked below high-probability predictions, you will still get a good score.

\n

Look up area under curve and see the Kaggle wiki.

", "votes": null, "replies": [] }, { "id": 50386, "author_name": "trisco7", "author_url": "", "post_date": "07/12/2014 08:59:59", "content": "

To Triskelion,

\n

As pointed out by rashmirin, "the repeater column in trainingHistory.csv contains only 0 or 1".

\n

I was building a model considering the repeater column as the CLASS label. So my testing data when submitted to that model, would return either 0 or 1.

\n

But now as pointed out by you, we have to predict the probability which can take any value between 0 and 1.

\n

So it means that after applying some pre processing on my training data, each repeater column indicating 1 value should return a high prob value and each repeater column indicating 0 in training data should return a low prob value.

\n

I am just a beginner in data analysis. Any guidance on this would be highly appreciated.

", "votes": null, "replies": [] }, { "id": 50396, "author_name": "ejlok1", "author_url": "", "post_date": "07/12/2014 12:49:56", "content": "

@Trisco7

\n

Not sure what you mean there but the Target / label doesn't change. It remains 0 and 1. But what Triskelion is advising is to submit probabilities of between 0 and 1. Where 1 is 100% confidence that the likelihood of the label is 1.

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "50185": "", "50239": "", "50241": "", "50243": "", "50262": "", "50268": "", "50331": "", "50364": "", "50369": "", "50386": "", "50396": "" }, "source": "meta" }