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Table 6: Training Dataset for Car Type Table 6. Training dataset for predicting car type from work years and college years. Instance Work Years College Years Car Type 1 4 5.5 Sports 2 5 3 Luxury 3 4 3.5 Family 4 3.5 4.5 Hybrid 5 5 4 Sports 6 1 4 Luxury 7 4 6 Family 8 6 4 Hybrid 9 3 3 Sports 10 4.5 4 Luxury 11 3 2 Family 12 4 2 Hybrid   Review the table labeled Table 6: Training Dataset for Car Type. You decide to use the K-Nearest Neighbors (KNN) model on the dataset to predict car type. Your friend worked for 4 years and attended college for 4 years. If K is set to 7 and the model uses Euclidean distance, which car type will be predicted for your friend?

Table 3: Training Dataset for Pet Type Table 3. Training dataset for predicting pet type from employment, age, debt, and college degree status. Employed Age Debt College Degree Pet Yes 26 No Yes Cat Yes 45 Yes No Dog No 22 Yes Yes Dog No 50 No Yes Cat Yes 24 No Yes Cat No 33 Yes Yes Dog Yes 62 No No Cat Yes 39 No Yes Dog No 25 No Yes Dog   Review the table labeled Table 3: Training Dataset for Pet Type. Assume we want to use a decision tree to predict a person’s pet type. Using misclassification error as the measure of node impurity, which attribute provides the best split?

Table 5: Cancer Cell Classification Table 5. Confusion matrix for cancer cell classification results. Predicted ClassCancerNormalActual ClassCancer25045Normal4291875   Review the table labeled Table 5: Cancer Cell Classification. You trained a model for detecting cancer cells in medical images, and the table contains the classification results. If the positive class is cancer cells, what is the accuracy and F1 score of the classification?

Table 9: Dataset for Animal Type Table 9. Dataset for predicting animal type from color, continent, and food. Color Continent Food Type Black Asia Meat Mammal Brown Europe Meat Mammal Black America Vegetables Mammal Green Africa Meat Mammal Black Asia Vegetables Mammal Black Africa Vegetables Bird Brown Europe Vegetables Bird Grey Asia Meat Mammal Brown America Meat Bird Green Asia Meat Mammal   Review the table labeled Table 9: Dataset for Animal Type. You trained a Naive Bayes Classifier on the dataset. If a record contains the values (Black, Africa, Vegetables), what Type would the model predict?

Table 2: Training Dataset for Car Ownership Table 2. Training dataset for predicting car ownership from employment, insurance, and marital status. Instance Employed Insured Marital Status Car Ownership 1 Yes No Single Yes 2 Yes Yes Single No 3 No No Married Yes 4 No Yes Single Yes 5 Yes Yes Married No 6 No No Single No Review the table labeled Table 2: Training Dataset for Car Ownership. Assume we want to use a decision tree to predict if a person owns a car or not. Using entropy as the measure of node impurity, what is the information gain if the split is done on the attribute of being employed?

Table 4: Transactions Dataset Table 4. Transaction dataset showing transaction IDs and itemsets for rule mining. Transaction ID Items Bought 1 {a, b, d, e} 2 {b, c, d} 3 {a, b, d, e} 4 {a, c, d, e} 5 {b, c, d, e} 6 {b, d, e} 7 {c, d} 8 {a, b, c} 9 {a, d, e} 10 {b, d}   Review the table labeled Table 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the confidence for the rule {d} → {b}?

Table 4: Transactions Dataset Table 4. Transaction dataset showing transaction IDs and itemsets for rule mining. Transaction ID Items Bought 1 {a, b, d, e} 2 {b, c, d} 3 {a, b, d, e} 4 {a, c, d, e} 5 {b, c, d, e} 6 {b, d, e} 7 {c, d} 8 {a, b, c} 9 {a, d, e} 10 {b, d}   Review the table labeled Table 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the support for the rule {d} → {b}?

Table 4: Transactions Dataset Table 4. Transaction dataset showing transaction IDs and itemsets for rule mining. Transaction ID Items Bought 1 {a, b, d, e} 2 {b, c, d} 3 {a, b, d, e} 4 {a, c, d, e} 5 {b, c, d, e} 6 {b, d, e} 7 {c, d} 8 {a, b, c} 9 {a, d, e} 10 {b, d}   Review the table labeled Table 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the support for the rule {a} → {c}?

Given the AR (1) estimation: Yt = 1.950 + 0.341 Yt-1, SE (1.950) = 0.322, SE (0.341) = 0.221.  The tabulated value of t at 5% level of significance is 1.96. Is the lag value of Y a useful predictor of the current Y?

a. What is the sign of Skewness and the minimum value of Kurtosis for the following density function with a normal fit?     b. The STATA results are given below for the Skewness and Kurtosis tests for normality for the above density function. Interpret the results. Do you reject the null hypothesis?         Joint test Variable Obs. Pr(skewness) Pr(kurtosis) Adj chi2(2) Prob>Chi2 birthweight 3,000 0.0000 0.0000 358.77 0.0000