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Question for for Data Science for Python Let x=[0, .1, .2, ...., .9, 1.0] and y=[0,0,0,0,0,1,1,1,1,1,1]. Fit the ordinary least square linear model (OLS), the
Question for for Data Science for Python
Let x=[0, .1, .2, ...., .9, 1.0] and y=[0,0,0,0,0,1,1,1,1,1,1]. Fit the ordinary least square linear model (OLS), the Ridge model (RIDGE) with alpha=0.5, and the logistic regression model (LR) into these data and calculate the fitting score for each of the models. What is the relationships between the models' scores?
a. | score(RIDGE) < score(OLS) < score(LR) | |
b. | score(OLS) < score(RIDGE) < score(LR) | |
c. | score(LR) < score(RIDGE) < score(OLS) | |
d. | score(LR) < score(OLS) < score(RIDGE) |
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