Compute the simple matching and Jaccard coefficient between X and Y: X = (1, 0, 1, 0) Y = (1, 1, 0, 0)

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

You are asked to train a model for detecting fraudulent bank transactions. The bank studies show that the bank sustains more damage when a fraudulent transaction is approved compared to when a normal transaction is blocked. For the bank to sustain less damage, which cost matrix should you use to tune your model? (F stands for Fraudulent, and N stands for Normal.)

You have a dataset of 100,000 dog images comprised of 100 different breeds. If you want to use a Support Vector Machine (SVM) for a classification task, which approach should you choose and why?

Figure 2: Training Dataset for Pet Type Employed Age Debt College Degree Pet Yes 26 Yes No Cat Yes 45 No Yes Dog No 22 Yes Yes Dog No 50 Yes No Cat Yes 24 Yes No Cat No 33 Yes Yes Dog Yes 62 No No Cat Yes 39 Yes No Dog No 25 Yes No Dog   Review the table labeled Figure 2: 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?

You are asked to train a model for detecting cancer tumors in medical images. The hospital studies show that patients suffer more harm when a cancer tumor goes undiagnosed compared to when a benign tumor is treated for cancer. For patients to suffer less harm, which cost matrix should you use to tune your model? (F stands for Found Tumor, and N stands for No Tumor.)

Three classification models were trained on a dataset of previous medical images with the goal of predicting cancer cells. The first model (M1), which is complex, has a training error of 0.0012. The second model (M2), which is simpler, has a training error of 0.0021. The third model (M3), which is the simplest, has a training error of 0.1108. Which model would be the best one to use for classifying the medical images of future patients?

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)