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NEW QUESTION 38
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......
- A. Both 1 and 2 are correct
- B. Categorical
- C. Numerical
- D. None of the 1 and 2 are correct
Answer: A
Explanation:
Explanation
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.
NEW QUESTION 39
Select the statement which applies correctly to the Naive Bayes
- A. Sensitive to how the input data is prepared
- B. Works with a small amount of data
- C. Works with nominal values
Answer: A,B,C
NEW QUESTION 40
In which lifecycle stage are appropriate analytical techniques determined?
- A. Discovery
- B. Model planning
- C. Data preparation
- D. Model building
Answer: B
Explanation:
Explanation
In Phase 3, the data science team identifies candidate models to apply to the data for clustering, classifying, or finding relationships in the data depending on the goal of the project, It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became acquainted with the data and understanding the business problems or domain area. These hypotheses help the team frame the analytics to execute in Phase
4 and select the right methods to achieve its objectives.
Some of the activities to consider in this phase include the following: Assess the structure of the datasets. The structure of the datasets is one factor that dictates the tools and analytical techniques for the next phase.
Depending on whether the team plans to analyze textual data or transactional data, for example, different tools and approaches are required.
Ensure that the analytical techniques enable the team to meet the business objectives and accept or reject the working hypotheses. Determine if the situation warrants a single model or a series of techniques as part of a larger analytic workflow. A few example models include association rules and logistic regression Other tools, such as Alpine Miner, enable users to set up a series of steps and analyses and can serve as a front-end user interface (Ul) for manipulating Big Data sources in PostgreSQL.
NEW QUESTION 41
What describes a true limitation of Logistic Regression method?
- A. It does not handle missing values well.
- B. It does not handle redundant variables well.
- C. It does not handle correlated variables well.
- D. It does not have explanatory values.
Answer: A
NEW QUESTION 42
Support vector machines (SVMs) are a set of supervised learning methods used for
- A. Non-linear classification
- B. Regression
- C. Linear classification
Answer: A,B,C
Explanation:
Explanation
In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.
NEW QUESTION 43
You are working on a Data Science project and during the project you have been gibe a responsibility to interview all the stakeholders in the project. In which phase of the project you are?
- A. Executing Models
- B. Operationnalise the models
- C. Creating Models
- D. Data Preparations
- E. Creating visuals from the outcome
- F. Discovery
Answer: F
Explanation:
Explanation
During the discovery phase you will be interviewing all the project stakeholders because they would be having quite a good amount of knowledge for the problem domain you will be working and you also interviewing project sponsors you will get to know what all are the expectations once project get completed. Hence, you will be noting down all the expectations from the project as well as you will be using their expertise in the domain.
NEW QUESTION 44
In which of the scenario you can use the linear regression model?
- A. Predicting demand of the goods and services based on the weather
- B. Predicting sales of the text book based on the number of students in state
- C. Predicting tumor size reduction based on input as number of radiation treatment
- D. Predicting Home Price based on the location and house area
Answer: A,B,C,D
Explanation:
Explanation : You can use the linear regression model for predicting the continuous output variable based on the input variables. In all the cases mentioned in the question option, you can see that output can be predicted based on the input variable.
Option-A: Input: Location, House Area and Output: House Price
Option-B : Input: Weather condition, Output: Demand for the goods and services Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction Option-D : Input: Number of students and Output: Sale quantity of text book
NEW QUESTION 45
Which of the following is a Continuous Probability Distributions?
- A. Negative binomial distribution
- B. Normal probability distribution
- C. Binomial probability distribution
- D. Poisson probability distribution
Answer: B
NEW QUESTION 46
Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?
- A. Linear regression
- B. K-means clustering
- C. Logistic regression
- D. Naive Bayesian classification
- E. Semi Linear Regression
Answer: B
Explanation:
Explanation
k-means clustering is a method of vector quantization^ originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.
NEW QUESTION 47
Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response. Which of the following method you should not use to identify whether the response is valid or not?
- A. Logistic Regression
- B. Naive Bayes
- C. Random Decision Forests
- D. Any one of the above
Answer: D
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
* Support vector machines
* Naive Bayes
* Logistic regression
* Random decision forests
NEW QUESTION 48
Projecting a multi-dimensional dataset onto which vector has the greatest variance?
- A. first eigenvector
- B. second principal component
- C. second eigenvector
- D. not enough information given to answer
- E. first principal component
Answer: E
Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the data onto the first principal component line, the data is more spread out (higher variance) than if projected onto any other line, including other principal components.
NEW QUESTION 49
Let's say you have two cases as below for the movie ratings
1. You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars
2. You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?
- A. In both cases, the contribution to the RMSE, could varies
- B. In both cases, the contribution to the RMSE is the same
- C. None of the above
- D. In both cases, the contribution to the RMSE is the different
Answer: B
NEW QUESTION 50
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
- A. P(E1,E2,E3)P(E2)P(E3)
- B. P(E1,E2|E3)P(E3)
- C. P(E1,E2,E3)P(E1)/P(E2:E3)
- D. P(E1,E2;E3)/P(E2,E3)
- E. P(E1,E2|E3)P(E2|E3)P(E3)
Answer: D
Explanation:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so P(E1|E2) = P(E1.E2)/P(E2) P(E1,E2,E3)/P(E2,E3) If the events are A and B respectively, this is said to be "the probability of A given B" It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.
NEW QUESTION 51
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Apriori Algorithm
- B. Ridge and Lasso
- C. Integer programming
- D. Ordinary Least squares
Answer: D
Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters
NEW QUESTION 52
Refer to exhibit
You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1.
Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C.
The results of the regression are seen in the exhibit. You cannot request additional data. what is a way that you could try to increase the R2 of the model without artificially inflating it?
- A. Force all 15 variables into the model as independent variables
- B. Break variables A, B, and C into their own univariate models
- C. Create clusters based on the data and use them as model inputs
- D. Create interaction variables based only on variables A, B, and C
Answer: C
Explanation:
Explanation
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X.
The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression^ where multiple correlated dependent variables are predicted, rather than a single scalar variable.) In linear regression data are modeled using linear predictor functions, and unknown model parameters are estimated from the data.
Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X.
Less commonly: linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X.
Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X:
which is the domain of multivariate analysis.
NEW QUESTION 53
Select the correct statement regarding the naive Bayes classification
- A. Independent variables can be assumed
- B. only the variances of the variables for each class need to be determined
- C. for each class entire covariance matrix need to be determined
- D. it only requires a small amount of training data to estimate the parameters
Answer: A,B,D
Explanation:
Explanation
An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.
NEW QUESTION 54
Refer to the Exhibit.
In the Exhibit, the table shows the values for the input Boolean attributes "A", "B", and "C". It also shows the values for the output attribute "class". Which decision tree is valid for the data?
- A. Tree B
- B. Tree C
- C. Tree D
- D. Tree A
Answer: A
NEW QUESTION 55
Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow.
Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)
- A. marginal probability that P(H=Not Hit) is the sum of the H= Hit row
- B. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
- C. The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.
Answer: B,C
Explanation:
Explanation
The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green. Similarly, the marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
NEW QUESTION 56
Which is an example of supervised learning?
- A. SVD
- B. PCA
- C. k-means clustering
- D. SVM
- E. EM
Answer: D
Explanation:
Explanation
SVMs can be used to solve various real world problems:
* SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
* Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
* SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
* Hand-written characters can be recognized using SVM
NEW QUESTION 57
In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because
- A. The normalizing constant is always very close to 1
- B. The normalizing constant only has a small impact on the maximum likelihood
- C. The normalizing constant doesn't impact the maximizing value
- D. The normalizing constant is often zero and can cause division by zero
Answer: C
Explanation:
Explanation
(Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.
NEW QUESTION 58
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