You have a dataset that contains information about different airplanes. For each airplane, more than 17 attributes, such as speed, capacity, price, brand, and so on, are available. You are asked to create a visual representation of this dataset for college students. What would NOT be a possible issue for learning models on this dataset?

Figure 7: Artificial Neural Network Dataset Instance 1 2 3 4 5 6 7 8 9 Actual Class 1 1 -1 1 1 -1 1 1 -1 Predicted Class 1 -1 -1 -1 -1 -1 -1 1 -1   Review the table labeled Figure 7: Artificial Neural Network Dataset. A forward pass of an Artificial Neural Network is finished and the data are classified in the table. What is the total sum of the squared errors?

Compute the cosine similarity and correlation coefficient between feature vectors X and Y: X = (1, 5, 2, 4) Y = (4, 4, 2, 6)

Figure 8: Dataset for Animal Type 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 Figure 8: 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?

Select all possible columns that can be set as class label for a data mining task.   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  

Figure 8: Dataset for Animal Type 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 Figure 8: Dataset for Animal Type. You trained a Naive Bayes Classifier on the dataset. If a record contains the values (Brown, America, Meat), what Type would the model predict?

You have a dataset of 80,000 car images comprised of 80 different brands. If you want to use a Support Vector Machine (SVM) for a classification task, which approach should you choose and why?

Figure 5: Training Dataset for Car Type 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 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?

What is a disadvantage of using the K-Nearest Neighbors (KNN) model for classification?

Three classification models were trained on a dataset of previous bank transactions with the goal of detecting fraudulent transactions. The first model (M1), which is complex, has a training error of 0.0034. The second model (M2), which is simpler, has a training error of 0.0047. The third model (M3), which is the simplest, has a training error of 0.1251. Which model would be the best one to use for classifying future transactions?