Logistic Regression: math domain error - sigmoid function give value 1

Question

I'm trying out logistic regression. While running the code, i get math domain error in python. Found out that i'm inputting (mx+b > 38) values large than 38 into the sigmoid function and it outputs 1, and the log function (-log(1-1)) spits out "math domain error".

Here are my steps:

Find mx+b
input mx+b as x in the sigmoid function
Input the value from sigmoid, y-value, x-value to the cost function
Find gradient from the above values.
Optimize the weights using gradient value.

Please help.

score 1 · Accepted Answer · answered Nov 22 '19 at 11:07

1

You should normalize your data before putting it into logistic function. Normalization means putting values in [0, 1] range, therefore you should not be getting 1's as outputs from sigmoid anymore. You can use this function for normalization: sklearn.preprocessing.normalize

answered Nov 22 '19 at 11:07

Ach113

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By the way, do you think the above mentioned steps to approach logistic regression is correct ? – Deepak S.M Nov 22 '19 at 11:14
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Seems like a correct approach, but I am definitely not an expert on this subject – Ach113 Nov 22 '19 at 11:17

Logistic Regression: math domain error - sigmoid function give value 1

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