Would anyone be able to assist me with this multiple choice question about classifier ? Thanks a lot!

Consider a classification problem where we are building a logistic regression classifier. The task is to predict if a given city has a risk of a disease epidemic or not. The data is defined using two input features or variables - logarithm of size of the city distance to the nearest city with epidemic The target is a binary (0 - no risk and 1 - risk). Consider the following training data set Assume that a bias term is added in the beginning. Consider the following weight vector for logistic regression: w_0 = 1.05. w_1 = -.52. w_2 = 0.85. Answer the following: The classifier's worst wrong prediction is for city #9 The classifier's worst wrong prediction is for city #10 The classifier is over 90% confident for 3 cities only The classifier's worst wrong prediction is for cities #5 and #7 Consider a classification problem where we are building a logistic regression classifier. The task is to predict if a given city has a risk of a disease epidemic or not. The data is defined using two input features or variables - logarithm of size of the city distance to the nearest city with epidemic The target is a binary (0 - no risk and 1 - risk). Consider the following training data set Assume that a bias term is added in the beginning. Consider the following weight vector for logistic regression: w_0 = 1.05. w_1 = -.52. w_2 = 0.85. Answer the following: The classifier's worst wrong prediction is for city #9 The classifier's worst wrong prediction is for city #10 The classifier is over 90% confident for 3 cities only The classifier's worst wrong prediction is for cities #5 and #7