Figure 5: Training Dataset for Car Type 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 Figure 5: 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?

Figure 6: Dataset for Pet Type Instance Age Home Value ($Millions) Pet 1 20 1.6M Cat 2 25 2.1M Cat 3 30 1.7M Cat 4 40 1.9M Cat 5 40 1.5M Dog 6 50 1.7M Dog 7 55 1.1M Dog 8 60 1.6M Dog   Review the table labeled Figure 6: Dataset for Pet Type. If you train a Support Vector Machine (SVM) on the dataset to predict pet type, the result will include how many support vectors?

Figure 4: Transactions Dataset 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 Figure 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the confidence for the rule {a} → {c}?

Figure 4: Transactions Dataset 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 Figure 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the confidence for the rule {d} → {b}?

Which type of deep neural network would work for the task of predicting stock market prices?

Figure 4: Transactions Dataset 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 Figure 4: Transactions Dataset. You are asked to use rule mining on the dataset. What is the support for the rule {d} → {b}?

You want to train a deep neural network for detecting bald eagles in nature pictures. Which type of network is the most suitable for this scenario?

Given that a set of data has noise and that the clusters are of different shapes and sizes, which clustering algorithm would work for this scenario?

Which type of deep neural network would work for the task of predicting gold market prices?

What process results in a Markov chain?