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?

You trained a model to predict stock market prices. Your model performs well on your dataset, but performs poorly on a different dataset. How can you improve your model?

You want to train a model for detecting stones that contain gold. A mining company has a dataset of 5 million stone images, of which only 500 contain gold. What is the best measure to evaluate your model if you designate stone images with gold as the positive class?

You have a dataset of 5,000 audio files, and you want to train a model to detect music files within the dataset. How would you validate the performance of your classifier?

You are given a dataset of cars and asked to train a decision tree to classify the cars into two classes: Luxury and Sports. If a leaf node contains 100 cars, which case would provide better performance? (L represents the Luxury class, and S represents the Sports class.)