Assume you would like to develop a classification model to predict COVID-19 based on the symptomfeatures. You have a dataset of 100 patient records to consider in the training process. Each recorddescribe a patient by their attributes ofls-Fever, Is-Tiredness, and Is-Cough. Each attribute has two valuesofT(indicating True) and F(indicating False). The class label tells us whether a patient gets COVID.19 infection, where .. " represents False, and "+'represents True. The following table summarizes thepatient dataset and two class labels +,-"

Assume you would like to develop a classification model to predict COVID-19 based on the symptom features. You have a dataset of 100 patient records to consider in the training process. Each record describe a patient by their attributes of Is-Fever, Is-Tiredness, and Is-Cough. Each attribute has two values of ' T ' (indicating True) and 'F' (indicating False). The class label tells us whether a patient gets COVID19 infection, where '- 'represents False, and '+' represents True. The following table summarizes the patient dataset and two class labels ' + ', '-'. a) Construct a decision tree classifier based on the records of 100 labeled patients. Calculate the gain in the Gini index when splitting on attributes Is-Fever and Is-Tiredness, respectively. According to the Gini index based gains, which one will you choose as the first attribute to split in the decision tree induction? b) Write a completed decision tree using the CART algorithm, which uses the Gini index based impurity measurement. c) Calculate the training instances are misclassified by the resulting decision tree. d) Predict the class labels of three new patients X, Y, and Z below, according to your constructed decision tree classifier