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Shown below are the electronic transitions that produce visible light in the hydrogen atom line spectrum.  (WCSU25)

Q1: Elaborate on the quoted words  below, taken from chapter 4.  -What do they refer to? (Give context to show your knowledge of the chapter in 2-3 sentences/5 marks).  -What do they reveal to you, the reader, about Noah? (Select and expand on at least one point in 4-5 sentences/5 marks). “Maybe I didn’t look like you, but if I spoke like you, I was you”.(88)

Q2: Focus on Noah’s descriptive style and use of figurative language in Chapter 4.  Choose ONE main descriptive use of language that was particularly effective for you. Explain why it affected you and how it made you think or question in 3-4 sentences. You may refer to the visual as a reminder, or choose any other that your remember.

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 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 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 8: Artificial Neural Network Dataset Table 8. Artificial neural network classification results showing actual and predicted class values by instance. 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 Table 8: Artificial Neural Network Dataset. A forward pass of an Artificial Neural Network finished and the data are classified in the table. What is the total sum of the squared errors?

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 using rule mining on a large dataset, and the table is a part of the dataset. If the itemset {c,e} is determined to be infrequent, which of the transactions in the table are also infrequent?

Select all possible columns that can be set as class label for a predictive 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  

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 9: Dataset for Animal Type Table 9. Dataset for predicting animal type from color, continent, and food. 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 Table 9: 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?