Question: bjectives Apply probabilistic statistical Bayes rule for classification task. In particular, given a set of labelled training data, use the Nave Bayes classifier to predict

bjectives Apply probabilistic statistical Bayes rule for classification task. In particular,

bjectives Apply probabilistic statistical Bayes rule for classification task. In particular, given a set of labelled training data, use the Nave Bayes classifier to predict class labels for test (i.e., unseen) instances. estions/Tasks a) Briefly explain Bayesian classifiers? What does Nave Bayes classifier mean and what is its nave assumption? Explain why do we need such an assumption? b) Consider the dataset training data shown in Table 1. The data consists of clinical features (i.e., four features) for several patients, relating several conditions with the existence of the coronary disease. Using the data in Table 1, determine the parameters of a Naive Bayes classifier. Indicate the most relevant calculations. ble 1: Training instances for 14 patients, Red rows indicate patients with coronary disease, while green rows are for tient without coronary disease diagnose. Pat. N. Hypertension Cholesterol Lev. Smoker Weight Coronary Dis. ID001 "Yes" "Normal" "No" "Overweight" "Yes" IDOO2 "No" "Normal" "Yes" "Normal" "No" ID003 "No" "Critical" "No" "Overweight" "Yes" ID004 "No" "High" "Yes" "Overweight" "Yes" ID005 "Yes" "Critical" "Yes" "Obese" "Yes" ID006 "Yes" "High" "Yes" "Normal" "Yes" ID007 "No" "High" "No" "Obese" "No" ID008 "Yes" "Normal" "Yes" "Normal" "Yes" ID009 "Yes" "Critical" "NO" "Obese" "Yes" ID010 "No" "Normal" "No" "Overweight" "No" ID011 "No" "Critical" "Yes" "Normal" "Yes" ID012 "Yes" "High" "No" "Overweight" "No" ID013 "Yes" "Normal" "Yes" "Overweight" "Yes" ID014 "Yes" "High" "No" "Obese" "No

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