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NEW QUESTION 54
Classification and regression are examples of___________.
- A. Clustering
- B. Density estimation
- C. un-supervised learning
- D. supervised learning
Answer: D
Explanation:
Explanation
In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points.
Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.
NEW QUESTION 55
You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use
- A. Clustering
- B. Linear Regression
- C. Logistic Regression
- D. PCA (Principal component analysis)
Answer: D
Explanation:
Explanation
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.
NEW QUESTION 56
Which of the following problem you can solve using binomial distribution
- A. It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between
20.03 mm and 20.08 mm - B. A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies
- C. Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
- D. A manufacturer of metal pistons finds that on the average: 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
Answer: D
Explanation:
Explanation
The entire problem can be solved using below method
Binomial: A manufacturer of metal pistons finds that on the average, 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
Poisson: A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies Poisson: Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
Normal: It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20 03 mm and 20.08 mm
NEW QUESTION 57
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...
- A. None of the above
- B. L1 gives Non-sparse output while L2 gives sparse outputs
- C. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
- D. L2 is the sum of the square of the weights, while L1 is just the sum of the weights
Answer: D
Explanation:
Explanation
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:
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NEW QUESTION 58
Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X
- A. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.
- B. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y
- C. This is typically calculated by summing the joint probability distribution over Y.
- D. This is typically calculated by integrating the joint probability distribution over Y
Answer: A,B,C,D
Explanation:
Explanation
Given two random variables X and Y whose joint distribution is known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. This is typically calculated by summing or integrating the joint probability distribution over Y. ' For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is Text Description automatically generated with low confidence
where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out.
Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables.
Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is Diagram Description automatically generated with medium confidence
where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.
Note that a marginal probability can always be written as an expected value:
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Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general A picture containing diagram Description automatically generated
NEW QUESTION 59
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by
- A. Poisson
- B. Any of the above
- C. Binomial
- D. Normal
Answer: C
Explanation:
Explanation
In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.
This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).
NEW QUESTION 60
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).
- A. 2/3
- B. 1/6
- C. 1/3
- D. 2/6
Answer: B
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION 61
Refer to Exhibit
In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?
- A. Discriminant Analysis
- B. Association Rules
- C. Logistic Regression
- D. Linear Regression
Answer: C
NEW QUESTION 62
Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.
- A. Naive Bayes
- B. All of the above
- C. Support vector machines
- D. Random decision forests
- E. Logistic regression
Answer: B
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 63
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie's wedding?
- A. All of the above
- B. Naive Bayes
- C. Random Decision Forests
- D. Logistic Regression
Answer: B
Explanation:
Explanation
The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following conditions exist.
* The sample space is partitioned into a set of mutually exclusive events {A1, A2,... :An}.
* Within the sample space, there exists an event B: for which P(B) > 0.
* The analytical goal is to compute a conditional probability of the form: P( Ak B).
NEW QUESTION 64
Which of the following could be features?
- A. Symptoms of a diseases
- B. 0nly 1 and 2
- C. Characteristics of an unidentified object
- D. Words in the document
- E. All 1,2 and 3 are possible
Answer: E
Explanation:
Explanation
Any dataset that can be turned into lists of features. A feature is simply something that is either present or absent for a given item. In the case of documents, the features are the words in the document but they could also be characteristics of an unidentified object symptoms of a disease, or anything else that can be said to be present of absent.
NEW QUESTION 65
Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?
- A. Run at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set
- B. Only consider values of k larger than log(N), where N is the number of observations in the data set
- C. Choose the initial centroids so that they are far away from each other
- D. Choose the initial centroids so that they all He along different axes
Answer: C
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 This Question-is about the properties that make k-means an effective clustering heuristic which primarily deal with ensuring that the initial centers are far away from each other. This is how modern k-means algorithms like k-means++ guarantee that with high probability Lloyd's algorithm will find a clustering within a constant factor of the optimal possible clustering for each k.
NEW QUESTION 66
Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,
- A. P(A,B|C) P(B|C) =P(C|B,C)
- B. P(A,B|C) P(B|C) =P(A|B,C)
- C. P(A,B|C) P(B|C) =P(B|A,C)
- D. P(A,B|C) P(B|C) =P(A|C,B)
Answer: B
Explanation:
Explanation
From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C) P(B,C) =P(A|BC) This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)
NEW QUESTION 67
You have data of 10.000 people who make the purchasing from a specific grocery store. You also have their income detail in the data. You have created 5 clusters using this data. But in one of the cluster you see that only 30 people are falling as below 30, 2400, 2600, 2700, 2270 etc." What would you do in this case?
- A. You will be increasing number of clusters.
- B. You will be decreasing the number of clusters.
- C. You will be multiplying standard deviation with the 100
- D. You will remove that 30 people from dataset
Answer: B
Explanation:
Explanation
Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.
NEW QUESTION 68
A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the
- A. Presence or absence of the other features
- B. None of the above
- C. Absence of the other features.
- D. Presence of the other features.
Answer: A
Explanation:
Explanation
In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.
NEW QUESTION 69
You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user. What kind of this recommendation engine is ?
- A. Collaborative filtering
- B. Naive Bayes classifier
- C. Logistic Regression
- D. Content-based filtering
Answer: A
Explanation:
Explanation
Another aspect of collaborative filtering systems is the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and help the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user
NEW QUESTION 70
Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.
Above is an example of
- A. Hierarchical linear models
- B. Recommendation system
- C. Logistic Regression
- D. Linear Regression
- E. Maximum likelihood estimation
Answer: C
Explanation:
Explanation : Logistic regression
Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values
NEW QUESTION 71
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