How to turn linear regression into logistical regression












1












$begingroup$


I followed these articles to implement logistic regression.



I'm confused however because after training the model and getting the weights for my variables I don't now how to use the one-hot vector to turn this into confidence scores for the different classes.



I've got the formula: y' = x1W1 + x2W2 + x3W3 + b



I've got values for all Ws and b.



I've got my one-hot vector: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]



How do I combine all this into confidence for each class?






machine-learning tensorflow logistic-regression







share|improve this question









$endgroup$

















    1












    $begingroup$


    I followed these articles to implement logistic regression.



    I'm confused however because after training the model and getting the weights for my variables I don't now how to use the one-hot vector to turn this into confidence scores for the different classes.



    I've got the formula: y' = x1W1 + x2W2 + x3W3 + b



    I've got values for all Ws and b.



    I've got my one-hot vector: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]



    How do I combine all this into confidence for each class?






    machine-learning tensorflow logistic-regression







    share|improve this question









    $endgroup$















      1












      1








      1





      $begingroup$


      I followed these articles to implement logistic regression.



      I'm confused however because after training the model and getting the weights for my variables I don't now how to use the one-hot vector to turn this into confidence scores for the different classes.



      I've got the formula: y' = x1W1 + x2W2 + x3W3 + b



      I've got values for all Ws and b.



      I've got my one-hot vector: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]



      How do I combine all this into confidence for each class?






      machine-learning tensorflow logistic-regression







      share|improve this question









      $endgroup$




      I followed these articles to implement logistic regression.



      I'm confused however because after training the model and getting the weights for my variables I don't now how to use the one-hot vector to turn this into confidence scores for the different classes.



      I've got the formula: y' = x1W1 + x2W2 + x3W3 + b



      I've got values for all Ws and b.



      I've got my one-hot vector: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]



      How do I combine all this into confidence for each class?






      machine-learning tensorflow logistic-regression




      machine-learning tensorflow logistic-regression






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 14 hours ago









      A.WhiteA.White

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      205






















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          $begingroup$

          You should use softmax to convert your output in probabilities. For only two classes, you have the formula $P(x in class 1) = frac{exp(y_{text{class1}})}{exp(y_{text{class1}}) + exp(y_{text{class2}})}$. It mentioned in your article.






          share|improve this answer









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            $begingroup$

            You should use softmax to convert your output in probabilities. For only two classes, you have the formula $P(x in class 1) = frac{exp(y_{text{class1}})}{exp(y_{text{class1}}) + exp(y_{text{class2}})}$. It mentioned in your article.






            share|improve this answer









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              2












              $begingroup$

              You should use softmax to convert your output in probabilities. For only two classes, you have the formula $P(x in class 1) = frac{exp(y_{text{class1}})}{exp(y_{text{class1}}) + exp(y_{text{class2}})}$. It mentioned in your article.






              share|improve this answer









              $endgroup$
















                2












                2








                2





                $begingroup$

                You should use softmax to convert your output in probabilities. For only two classes, you have the formula $P(x in class 1) = frac{exp(y_{text{class1}})}{exp(y_{text{class1}}) + exp(y_{text{class2}})}$. It mentioned in your article.






                share|improve this answer









                $endgroup$



                You should use softmax to convert your output in probabilities. For only two classes, you have the formula $P(x in class 1) = frac{exp(y_{text{class1}})}{exp(y_{text{class1}}) + exp(y_{text{class2}})}$. It mentioned in your article.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 11 hours ago









                Robin NicoleRobin Nicole

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