$6\mathrm{.}1.$ Run the program and complete the following exercises. $(20$ points$)$

$(3$ points$)$ We have learned two sklearn predefined models that solve linear problems with a regularization term

adding to the cost function. What are these models? Provide the function calls for each.

$(3$ points$)$ how does the hyperparameter

of the Ridge Regression model affect the model's variance and bias?

For the Logistic Regression example $($Titanic$),$

$(2$ points$)$ When we did "one hot encoding", why did we set "drop$\_$first" to "True"?

$(1$ points$)$ Briefly explain the command "titanic.drop$(\text{'}$survived$\_$yes', axis$=1)".($Hint: What did it do$?$ Why do we need it$?)$

$(1$ points$)$ In this model, what percentage of data are in the training set and the test set respectly?

$(3$ points$)$ What were the Precision, Recall, and F$1$Score for this model? $($Show your work$)$

For the Softmax Regression example $($Iris$),$

$(2$ points$)$ Is this model regulated? If it is regulated, how?

$(2$ points$)$ How did we turn the LogisticRegression model to a Softmax model?

$(2$ points$)$ If you find an iris with petals that are $5$cm long and $2$cm wide, what type of iris would you expect the model to tell you and with how much confidence $($the probability$)?($Show your work$)$