1.Question 1Which option lists the steps of training a logistic regression model in the correct order?Use the cost function on the training set.Update weights with new parameter values.Calculate cost function gradient.Initialize the parameters.Repeat until specified cost or iterations reached.1 point4, 1, 3, 2, 5 1, 4, 3, 2, 53, 2, 5, 4, 14, 3, 2, 5, 1

How are gradient descent and learning rate used in logistic regression? 1 pointGradient descent takes increasingly bigger steps towards the minimum with each iteration.Gradient descent will minimize learning rate to minimize the cost in fewer iterations.Gradient descent specifies the steps to take in the current slope direction, learning rate is the step length.We want to minimize the cost by maximizing the learning rate value.

1. Prove that the final parameter values for a logistic regression model (using a convex loss function L) found via gradient descent are located within the span of the input vectors. Assume that your initial parameter/feature vector is the zero vector. Hint: you can use a proof by induction to do this. 2. Consider the following training set: xy 12 24 48 5 10 Our goal is to find a regression model that could regress x to our target value y. Using linear regression model: y = w1x + w0 + ε = wT φ(x) + ε where φ(x) = [1, x]T and ε ∼ N (0, σε ) is 0-mean Gaussian noise. We call φ a feature mapping of x and this feature mapping allows us to absorb the bias w0 into the vector w. Using the closed formula compute w given the training set. (Hint: things should work out simply enough—this is not meant to be an exercise in solving linear systems by hand.)

In order to train a logistic regression model, we find the weights that maximize the likelihood of the model.Group of answer choicesTrueFalse

Concerning Classification algorithms, what are the characteristics of Logistic Regression?1 pointThe model is just parameters, fitting can be slow, prediction is fast, and the decision boundary is simple and less flexibleThe training data is the model, fitting is fast, prediction is fast, and the decision boundary is flexibleThe model is just parameters, fitting is fast, prediction is fast, and the decision boundary is flexibleThe training data is the model, fitting is fast, predicting class for new records can be slow, and the decision boundary is flexible

Logistic regression method: scope, examples