Apply the logistic regression with gradient descent and show only the first iteration of the algorithm. Use the learning rate =0.5, WO = 0.25. W1=2.5, W2= -3.5, W3=2.5, W X= wo + W1X1 + W2X2 + W3X3...


Apply the logistic regression with gradient descent and show only the first iteration of the algorithm. Use the learning rate =0.5,<br>WO = 0.25. W1=2.5, W2= -3.5, W3=2.5, W X= wo + W1X1 + W2X2 + W3X3 and assume no regularization is used. Find the<br>value of cost function and write the complete form of the hypothesis at the end of the first iteration.<br>No.of.Purchases<br>Is the Product<br>Popular?<br>No.of.Clicks<br>No.of.SavedWishlists<br>ProductiD<br>(X1)<br>(X2)<br>(X3)<br>P101<br>10<br>20<br>Yes<br>P102<br>15<br>15<br>No<br>P103<br>20<br>8<br>Yes<br>P104<br>15<br>10<br>Yes<br>P105<br>50<br>10<br>No<br>P106<br>20<br>10<br>2<br>No<br>P107<br>8<br>15<br>10<br>No<br>

Extracted text: Apply the logistic regression with gradient descent and show only the first iteration of the algorithm. Use the learning rate =0.5, WO = 0.25. W1=2.5, W2= -3.5, W3=2.5, W X= wo + W1X1 + W2X2 + W3X3 and assume no regularization is used. Find the value of cost function and write the complete form of the hypothesis at the end of the first iteration. No.of.Purchases Is the Product Popular? No.of.Clicks No.of.SavedWishlists ProductiD (X1) (X2) (X3) P101 10 20 Yes P102 15 15 No P103 20 8 Yes P104 15 10 Yes P105 50 10 No P106 20 10 2 No P107 8 15 10 No

Jun 10, 2022
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