Finding relationship between variables












0















There are two sets:

A: 1 2 3

B : 1 2 3 4 5 6 7 8 9 10

Points in A serve to multiple points in B. for example:

A 1: B 1 2 4

A 2: B 3 5 6

A 3: B 7 8 9 10



Given historical data of points in both A and B set, how to determine the which point in A is serving to points in set B?






data-science correlation supervised-learning unsupervised-learning







share|improve this question



























    0















    There are two sets:

    A: 1 2 3

    B : 1 2 3 4 5 6 7 8 9 10

    Points in A serve to multiple points in B. for example:

    A 1: B 1 2 4

    A 2: B 3 5 6

    A 3: B 7 8 9 10



    Given historical data of points in both A and B set, how to determine the which point in A is serving to points in set B?






    data-science correlation supervised-learning unsupervised-learning







    share|improve this question

























      0












      0








      0








      There are two sets:

      A: 1 2 3

      B : 1 2 3 4 5 6 7 8 9 10

      Points in A serve to multiple points in B. for example:

      A 1: B 1 2 4

      A 2: B 3 5 6

      A 3: B 7 8 9 10



      Given historical data of points in both A and B set, how to determine the which point in A is serving to points in set B?






      data-science correlation supervised-learning unsupervised-learning







      share|improve this question














      There are two sets:

      A: 1 2 3

      B : 1 2 3 4 5 6 7 8 9 10

      Points in A serve to multiple points in B. for example:

      A 1: B 1 2 4

      A 2: B 3 5 6

      A 3: B 7 8 9 10



      Given historical data of points in both A and B set, how to determine the which point in A is serving to points in set B?






      data-science correlation supervised-learning unsupervised-learning




      data-science correlation supervised-learning unsupervised-learning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Jan 1 at 21:10









      Prajakta GujarathiPrajakta Gujarathi

      31




      31
























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          Encode A and B columns as vectors and fit classification model. Then, after fitting, you can make predictions for various inputs of A ((1, 0, 0) as an example) and get probabilities in vector B ((0.25, 0.5, 0.1, ..., 0.15) as an example). So, in this case, value 1 for A serves values (1, 2, 3, 10) with probabilities above. Depending on the task, you can select some threshold.



          Depending on the data you need to select an encoding method (dummy vs one-hot), model, think about sampling, metric and so on.






          share|improve this answer























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            1 Answer
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            1 Answer
            1






            active

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            active

            oldest

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            Encode A and B columns as vectors and fit classification model. Then, after fitting, you can make predictions for various inputs of A ((1, 0, 0) as an example) and get probabilities in vector B ((0.25, 0.5, 0.1, ..., 0.15) as an example). So, in this case, value 1 for A serves values (1, 2, 3, 10) with probabilities above. Depending on the task, you can select some threshold.



            Depending on the data you need to select an encoding method (dummy vs one-hot), model, think about sampling, metric and so on.






            share|improve this answer




























              0














              Encode A and B columns as vectors and fit classification model. Then, after fitting, you can make predictions for various inputs of A ((1, 0, 0) as an example) and get probabilities in vector B ((0.25, 0.5, 0.1, ..., 0.15) as an example). So, in this case, value 1 for A serves values (1, 2, 3, 10) with probabilities above. Depending on the task, you can select some threshold.



              Depending on the data you need to select an encoding method (dummy vs one-hot), model, think about sampling, metric and so on.






              share|improve this answer


























                0












                0








                0







                Encode A and B columns as vectors and fit classification model. Then, after fitting, you can make predictions for various inputs of A ((1, 0, 0) as an example) and get probabilities in vector B ((0.25, 0.5, 0.1, ..., 0.15) as an example). So, in this case, value 1 for A serves values (1, 2, 3, 10) with probabilities above. Depending on the task, you can select some threshold.



                Depending on the data you need to select an encoding method (dummy vs one-hot), model, think about sampling, metric and so on.






                share|improve this answer













                Encode A and B columns as vectors and fit classification model. Then, after fitting, you can make predictions for various inputs of A ((1, 0, 0) as an example) and get probabilities in vector B ((0.25, 0.5, 0.1, ..., 0.15) as an example). So, in this case, value 1 for A serves values (1, 2, 3, 10) with probabilities above. Depending on the task, you can select some threshold.



                Depending on the data you need to select an encoding method (dummy vs one-hot), model, think about sampling, metric and so on.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Jan 2 at 8:38









                avchauzovavchauzov

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