There are four questions related to the same scenario in the test presented in random order. Consider a classification problem of a online customer as follows Predicted (output) variable is buy food or not (has two distinct values as yes and no). Input variables are: total food spending last year, age of the customer, categorical (3 levels): (young, middle, old) yearly income of customer, categorical (5 levles): (low, midle_low, middle, high_middle, high) marital status of the customer (marital status has four distinct values), customers occupation, (occupation has fife distinct values), Thermometer encoding is used properly. This problem is to be solved by multilayer feedforward neural network with a single hidden layer containing 5 nodes. What is the total number of weights? O a. 85 O b. 30 O c. 91 O d. 36

There are four questions related to the same scenario in the test presented in random order. Consider a classification problem of a online customer as follows Predicted (output) variable is buy food or not (has two distinct values as yes and no). Input variables are: total food spending last year, age of the customer, categorical (3 levels): (young, middle, old) yearly income of customer, categorical (5 levles): (low, midle_low, middle, high_middle, high) marital status of the customer (marital status has four distinct values), customers occupation, (occupation has fife distinct values), Thermometer encoding is used properly. This problem is to be solved by multilayer feedforward neural network with a single hidden layer containing 5 nodes. What is the total number of weights? O a. 85 O b. 30 O c. 91 O d. 36

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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There are four questions related to the same scenario in the test shown in random order. Based on a sample of 60 observations the population regression model to predict total yearly spending of an online customer for food from an online retail stories is based on the following inputs: total food spending last year, gender of the customer (two distinct values), yearly income of customer in TL, marital status of the customer (marital status has four distinct values), customers occupation, (occupation has six distinct values), Assume that all categorical variables affect only intercept of the linear regression model. Some figures in the ANOVA table are missing. S: d.f. MS Regression a b 20.0 Error Total 1000.0 d What is a? a. 220 О Б. 300 280 O d. 240
You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10
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1. Your friend has a strong opinion on cars: he either likes a car or does not like it at all. We have collected the following training data about his preferences using the features Color taking values: Red, Yellow; Type taking values: Sports, SUV; - Origin taking values: Domestic, Imported. No color likes origin Sports Domestic Yes Sports Domestic No Sports Domestic Yes Yellow Sports| Domestic No Yellow Sports| Imported Yes Imported No Imported Yes Domestic No Imported No Sports | Imported Yes type 1 Red 2 Red Red 4 5 6 Yellow SUV 7 Yellow SUV 8 Yellow SUV Red SUV 10 | Red Use a naive Bayes' classifier to predict whether your friend likes a Red Domestic SUV. Explain your computation in detail.
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Q3/ For a given exam, there is 40% of the students passed math test, and 30% passed biology test, and 20% passed both math and biology tests. What is the probability that a randomly selected student failed biology given that he passed math? (Use Venn diagram)
In a restaurant, customers usually may buy any number of meals at any time Several customers can also buy any specific meal. If we want to draw an ERD between 'Customer" and Meal," what will be the relationship, and why?
Select the answer below that best describes a dependent variable: It is the variable that we are using to predict the independent variable (response/explanatory, the z variable) It is the variable that we are trying to predict (outcome/response, the y variable) It is the variable that we are using to predict the dependent variable (predictor/explanatory, the x variable) It is the variable that is the quantitative variable (numeric whole/numeric decimal, the w variable)
In a recent study on world happiness, participants were asked to evaluate their current lives on a scale from 0 to 10, where O represents the worst possible life and 10 represents the best possible life. The responses were normally distributed, with a mean of 5.3 and a standard deviation of 1.9. Answer parts (a)-(d) below. (a) Find the probability that a randomly selected study participant's response was loss than 4. The probability that a randomly selected study participant's response was lons than 4 is (Round to four decimal placos as needed.)
Consider the following three simple rules to represent fuzzy relationship between food quality and service in a restaurant as input variables and tip amount as output variables. 1) If the food is bad OR the service is poor, then the tip will be low 2) If the service is acceptable, then the tip will be medium 3) If the food is great OR the service is amazing, then the tip will be high. Given the membership functions illustrated in Figure 1 and 2 as follows. Using the clipped center of gravity method, compute the tip amount based on the following circumstances: 1) Food quality was 6.0, and 2) Service was 8.0