Use the scikit learn library for all the models if possible

Using the Auto data set

$6.$ Alter the threshold for classifying a Low to $0\mathrm{.}6$ and report the changes in the test performance metrics from those reported in Qn $5.$

$7.$ Find the optimal threshold by drawing the ROC curve. Change the threshold to the optimal value you found from the ROC curve and report the changes in the test performance metrics from those reported in Qn $5.$

$8.$ Fit a Na$$ve Bayes model to the training data to predict mpg$\_$high$\_$low using all the other features$/$variables except mpg$,$ year, origin, and name. Predict the mpg$\_$high$\_$low using the test dataset. Plot the ROC curve and report the best threshold on the ROC curve plot. Report the AUC on the curve plot as well. Report the accuracy, precision, recall, specificity and F$1$ score.

$9.$ Fit a KNN model to the training data to predict mpg$\_$high$\_$low using all the other features$/$variables except mpg$,$ year, origin, and name. Use a grid search between $3$ and $10$ to find the best value of k$.$ Report the accuracy, precision, recall, specificity, F$1$ score and AUC.

$10.$ Fit a LDA model to the training data to predict mpg$\_$high$\_$low using all the other features$/$variables except mpg$,$ year, origin, and name. Report the accuracy, precision, recall, specificity and F$1$ score.

$11.$ Summarize the performance of the all the above models by creating a dataframe with $4$ columns $$ Model$\_$Name, Accuracy, Precision, Recall, Specificity, F$1$ Score. The data frame should contain one row for each model you built above with each of the columns filled in with the appropriate metric. Print out the dataframe. Which model performed the best from an accuracy point of view and which model performed best from a recall point of view without adjusting for the threshold?