QUESTION 2 (c ) To discuss how to evaluate the performance and

QUESTION 2
(c ) To discuss how to evaluate the performance and robustness of the logistic Regression model
for a given dataset.
A logistic regression being performed on a given dataset that contain information of Breast
cancer winscon (Diagnostic) to predict whether the cancer is benign or malignant. We are using
1010data function g_logreg(G;S;Y;XX;Z) being applied to Breast cancer winscon (Diagnostic)
Dataset which is obtained from UCI Machine Learning Repository. Its enables patients to know
whether the cancer is benign or malignant. Logistic regression uses various variables as stated
below in the dataset as predictors (Hosmer & Lemeshow, 2000). That is ID number, diagnosis,
radius-mean, texture-mean and perimeter-mean. For a response the column Y, which is yes if the
cancer is benign. The Breast cancer winscon (Diagnostic) dataset
(https://www.kaggle.com/uciml/breast-cancer-wisconsin-data#data.csv ), from this dataset we
create dummy variables for each of the five categorical columns. Since we would also want to
create a column separating training data and test data, we use 90% of the data as training data
and 10% of the data as test data. Based on the continuous variables in the original dataset and the
dummy variables we had created, we run the logistic regression model. The train column from
the previous step is then used as the second parameter of g_logreg(G;S;Y;XX;Z) function. Train
column acts as a selector so that the function will only train 90% of the data. We also specify
options z parameter to control convergence criteria. The logistic regression model or analysis is
therefore created using this dataset.
We then use score(XX;M;Z) function to predict probability that the cancer is benign using this
logistic regression analysis (Long et al., 2006). To obtain the model coefficients or constants we
use param(M;P;I) function to get b0 (the intercept) and the other variables b1, b2 and b3. We also