Vhich of the following research scenarios could be solved using a classification model: Predicting the time of rehabilitation for COVID-19 infected patients. Predicting the number of litres of beer consumed after level 3 lockdown restrictions have been implemented. Sorting an aerial photograph into urban and agricultural zones Fitting a generative model to a set of cat images and then generating a new cat image. Which statement regarding the assessment of a classification model is true? AUC graphs do not show the overall performance of a classifier. Recall refers to the percentage of total incorrect classifications by a model. The ROC curve can display both true positive and false positive rates at a range of thresholds. Precision is calculated as the the number of true positives divided by the sum of true positives and true negatives. What will the following piece of code output?

 from sklearn.metrics import classification_report print('Classification Report') print(classification_report(y_test, pred_im, target_names=['ham', 'spam'])) 

A 2x2 confusion matrix for classifying "ham" or "spam". A table of accuracies for "spam" and "ham". A matrix of for assessing precision, recall, f1-score and support for classifying "ham" or "spam", along with micro, macro and weighted averages for the model. A list of all the correct and incorrect classifications for "ham" or "spam" predicted by the model. Select the correct statements from the following: i) Sensitivity refers to the True Positive Rate ii) F1-Score is the harmonic mean between precision and recall iii) "LogisticRegression" can be imported from "sklearn.linear_model" iv) SVM and Random Forests can be used for classification i), ii), iii) All of the above ii), iii) 01 i), iii) Which one of the following statements is correct regarding the parameter C in logistic regression? C is directly proportional to the regularisation coefficient, lambda. C can be assigned a number less than or equal to zero. An increase in C will result in less regularisation. C can only be used for binary logistic regression