Given the following short movie reviews, each labeled with a genre, either comedy or action: Check The ImageCompute the most likely class for D. Assume a naive Bayes Classifier and use add-1 smoothing for the likelihoods. • fun, couple, love, love : comedyfast, furious, shoot ; actioncouple, fly, fast, fun, fun : comedyfurious, shoot, shoot, fun : action• fly, fast, shoot, love : actionand a new document D:fast, couple, shoot, fly

Answered: Image /qna-images/answer/f097130a-0473-45a2-ab3b-f2fc70ceca57.jpg

Given the following short movie reviews, each labeled with a genre, either comedy or action: Check The Image Compute the most likely class for D. Assume a naive Bayes Classifier and use add-1 smoothing for the likelihoods.

Given the following short movie reviews, each labeled with a genre, either comedy or action: Check The Image Compute the most likely class for D. Assume a naive Bayes Classifier and use add-1 smoothing for the likelihoods.

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

See similar textbooks

Similar questions

Question 3. Regression need answer of part b Consider real-valued variables X and Y. The Y variable is generated, conditional on X, from the fol- lowing process: E~N(0,0²) YaX+e where every e is an independent variable, called a noise term, which is drawn from a Gaussian distri- bution with mean 0, and standard deviation σ. This is a one-feature linear regression model, where a is the only weight parameter. The conditional probability of Y has distribution p(YX, a) ~ N(aX, 0²), so it can be written as p(YX,a) = exp(- (-202 (Y-ax)²) 1 ν2πσ The following questions are all about this model. MLE estimation (a) Assume we have a training dataset of n pairs (X, Y) for i = 1..n, and σ is known. Which ones of the following equations correctly represent the maximum likelihood problem for estimating a? Say yes or no to each one. More than one of them should have the answer "yes." a 1 [Solution: no] arg max > 2πσ 1 [Solution: yes] arg max II a [Solution: no] arg max a [Solution: yes] arg max a 1…
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 levels): (low, middle_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 nodes? а. 24 O b. 11 О. 22 O d. 20
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
We want to build a regression model and have many observations and many predictors. (a) From a computational point of view, which of these two model building algorithms are preferable: best subset selection or forward stepwise selection? (b) True or false and explain: Best subset selection will result in a smaller prediction error than forward stepwise selection because every model that is considered in forward stepwise selection is also considered in best subset se
Explain why linear predictor condition in predictor transformation is necessary.
Suppose we are trying to model a classification problem with two labels: 'sick' and 'healthy.' For the purpose of this test, we consider a positive result to be testing sick and a negative to test as healthy. After fitting the model with LDA in R, we compare predicted values from the actual values, as shown below: > lda. fit = 1da (test.result x1 + x2, data-mydata) > lda. pred > lda.class=lda.pred$class > table (lda.class, mydata$test.result) = predict (1da.fit) mydata lda.pred sick healthy sick 40 healthy 25 32 121
Mary: "Before we run the multivariate linear regression, feature scaling should be performed." Give one reason to support Mary's idea. Moreover, should we perform feature scaling before or after the gradient descent?
Question 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.
Why don't we use the ordinary least square to learn a linear regression model for a classification problem (i.e., learning to fit the label of 0 or 1)? Select one: a. Linear regression cannot output value in a probability range. b. OSL will learn a bad linear regression model for a classification. c. Yes, we can. Linear regression is the same as logistic regression. d. Linear regression outputs continuous variable.
Assume the following simple regression model, Y = β0 + β1X + ϵ ϵ ∼ N(0, σ^2 ) Now run the following R-code to generate values of σ^2 = sig2, β1 = beta1 and β0 = beta0. Simulate the parameters using the following codes: Code: # Simulation ## set.seed("12345") beta0 <- rnorm(1, mean = 0, sd = 1) ## The true beta0 beta1 <- runif(n = 1, min = 1, max = 3) ## The true beta1 sig2 <- rchisq(n = 1, df = 25) ## The true value of the error variance sigmaˆ2 ## Multiple simulation will require loops ## nsample <- 10 ## Sample size n.sim <- 100 ## The number of simulations sigX <- 0.2 ## The variances of X # # Simulate the predictor variable ## X <- rnorm(nsample, mean = 0, sd = sqrt(sigX)) Q1 Fix the sample size nsample = 10 . Here, the values of X are fixed. You just need to generate ϵ and Y . Execute 100 simulations (i.e., n.sim = 100). For each simulation, estimate the regression coefficients (β0, β1) and the error variance (σ 2 ). Calculate the mean of…
Given a two-category classification problem under the univariate case, where there are two training sets (one for each category) as follows: D₁ = (-3,-1,0,4} D₂ = {-2,1,2,3,6,8} Given the test example x = 5, please answer the following questions: have and a) Assume that the likelihood function of each category has certain paramétric form. Specifically, we p(x | w₁) N, 07) p(x₂)~ N(μ₂, 02). Which category should we decide on when maximum-likelihood estimation is employed to make the prediction?
Consider a machine learning problem for which the trainings data (x, y) are {(1, 4.8), (3, 11.3), (5, 17.2)). Using univariate linear regression, what is the corresponding hypothesis hex)? What is the predicted output for an attribute of x = 4? Show all the steps in the calculations.