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Mark all the correct statements: Assuming a logistic regression classifier and a datapoint currently classified as correct, and far away from the decision boundary. If this datapoint is removed, and the classifier retrained, the decision boundary does not change. Maximizing the likelihood of a logistic regression model yields multiple local optimums. We can get multiple local optimum solutions if we solve a linear regression problem by minimizing the sum of squared errors using gradient descent. In regularized logistic regression, if we want to prevent the weights from getting too big we can increase the regularization constant