Select all possible columns that can be set as class label for a data mining task. Table 1: Taxpayer Classification DatasetTable 1. Taxpayer dataset showing refund status, marital status, taxable income, and cheat classification. Tid Refund marital Status Taxable Income Cheat 1 Yes Single 125k No 2 No Married 100k No 3 No Single 70k No 4 Yes Married 120k No 5 No Divorced 95k Yes 6 No Married 60k No 7 Yes Divorced 220k No 8 No Single 95k Yes 9 No Married 75k No 10 No Single 90k Yes  

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 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 Hybrid 2 5 3 Sports 3 4 3.5 Luxury 4 3.5 4.5 Family 5 5 4 Hybrid 6 1 4 Sports 7 4 6 Luxury 8 6 4 Family 9 3 3 Hybrid 10 4.5 4 Sports 11 3 2 Luxury 12 4 2 Family 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: Second Dataset for X and Y Table 3. Dataset showing point numbers and X and Y values for K-means clustering. Point X Y 1 1 3 2 2 2 3 6 7 4 2.5 3.5 5 4 2 6 3 5 7 1 1 8 2 2 9 5 6 10 4 4 11 5.5 2.5   Review the table labeled Table 3: Second Dataset for X and Y. You decide to use the K-means algorithm on the dataset: K is set to 3 and, initially, Point 5 is the first centroid, Point 6 is the second centroid, and Point 7 is the third centroid. Using Euclidean distance measure, what are the clusters after the first iteration?

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 following AR(1) model with intercept and time trend: Yt = 0.162 + 0.001t – 0.80 Yt-1. The standard error of the coefficient of Yt-1 is 0.4. Test whether the series Yt is stationary or a random walk with trend. (DF statistic = -3.41 at 5% level of significance).

Using birthweight (in grams) -alcohol data, the following are the Wilcoxon Rank-Sum (Mann-Whitney) Test results. Interpret the results. (Note: D =1 if mother drank alcohol during pregnancy) Alcohol Obs. Rank sum Expected 0 2942 4426087 4414471 1 58 75413 87029 Combined 3000 4501500 4501500 Unadjusted variance 42673220   Adjustment for ties -6872.7978   Adjusted variance 42666347                       Z = 1.778 Prob > |z| = 0.0753

Given the AR (1) estimation: Yt = 1.950 + 0.341 Yt-1, SE (1.950) = 0.322, SE (0.341) = 0.331.  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?

What is the molarity of a solution made by dissolving 3.00 moles of NaCl in 1500 mL of water? ( 1L = 1000 mL)

Using the data given in the reaction below2Mg + O2 –> 2MgO ∆H= -465 kJ/molcalculate the enthalpy of the following reactionMgO –> Mg + 0.5O2