Table 3.1 shows the training data for predicting if a tumor is malignant or non- malignant based on tumor size. Table 3.1 Tumor Size (x) Malignant (y) 3 no 5 no 7 yes 9 yes Suppose logistic regression is used to perform tumor classification. Write down the hypothesis function hø(x) using parameters (0, 01) for the model. (i) = 0.5) and model B (0, =-1, 0, = 0.3), Given model A (0, = -3, 01 determine which model is a better fit for the training data in Table 3.1. Justify your answer by computing their respective classification accuracies. (ii) %3D %3D Suppose that gradient descent is used to train the model. Specify the update equation for logistic regression. (iii)
Table 3.1 shows the training data for predicting if a tumor is malignant or non- malignant based on tumor size. Table 3.1 Tumor Size (x) Malignant (y) 3 no 5 no 7 yes 9 yes Suppose logistic regression is used to perform tumor classification. Write down the hypothesis function hø(x) using parameters (0, 01) for the model. (i) = 0.5) and model B (0, =-1, 0, = 0.3), Given model A (0, = -3, 01 determine which model is a better fit for the training data in Table 3.1. Justify your answer by computing their respective classification accuracies. (ii) %3D %3D Suppose that gradient descent is used to train the model. Specify the update equation for logistic regression. (iii)
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Logistic Regression practice question
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Transcribed Image Text:Table 3.1 shows the training data for predicting if a tumor is malignant or non-
malignant based on tumor size.
Table 3.1
Tumor Size (x)
Malignant (y)
3
no
5
no
7
yes
9.
yes
Suppose logistic regression is used to perform tumor classification.
Write down the hypothesis function hg (x) using parameters (09, 01) for
the model.
(i)
Given model A (0, = -3, 0, = 0.5) and model B (0, = -1, 0, = 0.3),
determine which model is a better fit for the training data in Table 3.1.
Justify your answer by computing their respective classification
accuracies.
(ii)
Suppose that gradient descent is used to train the model. Specify the
update equation for logistic regression.
(iii)
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