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NEW QUESTION 80
Reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions. It is done in_______
- A. supervised learning
- B. un-supervised learning
- C. Support vector machines
- D. k-Nearest Neighbors
Answer: B
Explanation:
Explanation
The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions
NEW QUESTION 81
Select the correct option from the below
- A. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.
- B. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.
- C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or
+_to -_, then you need to look unsupervised learning - D. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.
- E. If you're not trying to predict a target value, then you need to look into unsupervised learning
Answer: A,B,D,E
Explanation:
Explanation
If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to
999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.
NEW QUESTION 82
Select the correct statement which applies to logistic regression
- A. Computationally inexpensive, easy to implement knowledge representation easy to interpret
- B. All 1, 2 and 3 are correct
- C. Only 1 and 3 are correct
- D. Works with Numeric values
- E. May have low accuracy
Answer: B
Explanation:
Explanation
Depending on the size of the data you are uploading, Amazon S3 offers the following options:
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 83
Question-13. Which of the following is not the Classification algorithm?
- A. None of the above
- B. Support Vector Machine
- C. Neural Network
- D. Hidden Markov Models
- E. Logistic Regression
Answer: A
Explanation:
Explanation
Logistic regression
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.
Support Vector Machines
As with naive Bayes, Support Vector Machines (or SVMs) can be used to solve the task of assigning objects to classes. But the way this task is solved is completely different to the setting in naive Bayes.
Neural Network
Neural Networks are a means for classifying multidimensional objects.
Hidden Markov Models
Hidden Markov Models are used in multiple areas of machine learning, such as speech recognition, handwritten letter recognition, or natural language processing.
NEW QUESTION 84
In which of the following scenario you should apply the Bay's Theorem
- A. Within the sample space, there exists an event B, for which P(B) > 0.
- B. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
- C. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
- D. In all above cases
Answer: D
NEW QUESTION 85
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 often zero and can cause division by zero
- B. The normalizing constant is always very close to 1
- C. The normalizing constant only has a small impact on the maximum likelihood
- D. The normalizing constant doesn't impact the maximizing value
Answer: D
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 86
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?
- A. P(E1+E2)/P(E1)
- B. P(E2)/P(E1)
- C. P(E1)/P(E2)
- D. P(E2)/(P(E1+E2)
Answer: B
NEW QUESTION 87
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 integrating 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 summing (In case of discrete variable) 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:
Text, letter Description automatically generated
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 88
Select the correct statement regarding the naive Bayes classification
- A. only the variances of the variables for each class need to be determined
- B. for each class entire covariance matrix need to be determined
- C. Independent variables can be assumed
- D. it only requires a small amount of training data to estimate the parameters
Answer: A,C,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 89
What describes a true property of Logistic Regression method?
- A. It works well with discrete variables that have many distinct values.
- B. It is robust with redundant variables and correlated variables.
- C. It handles missing values well.
- D. It works well with variables that affect the outcome in a discontinuous way.
Answer: B
NEW QUESTION 90
You are having 1000 patients' data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?
- A. You will be adding height with the numeric value 100
- B. You will be dividing both age and height with their respective standard deviation
- C. You will be converting each height value to centimeters
- D. You will be taking square root of height
Answer: B,C
Explanation:
Explanation
When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100.
While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum.
However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.
NEW QUESTION 91
You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer
- A. Transformation
- B. Differentiation
- C. Integration
- D. Generalization
Answer: C
Explanation:
Explanation
Text Description automatically generated
Text Description automatically generated
Text Description automatically generated
NEW QUESTION 92
Google Adwords studies the number of men, and women, clicking the advertisement on search engine during the midnight for an hour each day.
Google find that the number of men that click can be modeled as a random variable with distribution Poisson(X), and likewise the number of women that click as Poisson(Y).
What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour
?
- A. Poisson(X+Y)
- B. Binomial(X+Y,X+Y)
- C. Normal(X+Y(M+Y)1/2)
- D. Poisson(X/Y)
Answer: A
Explanation:
Explanation
The total number of clicks is the sum of the number of men and
women. The sum of two Poisson random variables also follows a Poisson distribution with rate equal to the sum of their rates.
The Normal and Binomial distribution can approximate the Poisson distribution in certain cases, but the expressions above do not approximate Poisson(X+Y).
NEW QUESTION 93
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. Logistic Regression
- D. Random Decision Forests
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 94
Select the sequence of the developing machine learning applications
A) Analyze the input data
B) Prepare the input data
C) Collect data
D) Train the algorithm
E) Test the algorithm
F) Use It
- A. C, B, A, D, E, F
- B. A, B, C, D, E, F
- C. C, A, B, D, E, F
- D. C, B, A, D, E, F
Answer: D
Explanation:
Explanation
1 Collect data. You could collect the samples by scraping a website and extracting data: or you could get information from an RSS feed or an API. You could have a device collect wind speed measurements and send them to you, or blood glucose levels, or anything you can measure. The number of options is endless. To save some time and effort you could use publicly available data
2 Prepare the input data. Once you have this data, you need to make sure it's in a useable format. The format we'll be using in this book is the Python list. We'll talk about Python more in a little bit, and lists are reviewed in appendix A.
The benefit of having this standard format is that you can mix and match algorithms and data sources. You may need to do some algorithm-specific formatting here. Some algorithms need features in a special format, some algorithms can deal with target variables and features as strings, and some need them to be integers. We'll get to this later but the algorithm-specific formatting is usually trivial compared to collecting data.
3 Analyze the input data. This is looking at the data from the previous task. This could be as simple as looking at the data you've parsed in a text editor to make sure steps 1 and 2 are actually working and you don't have a bunch of empty values. You can also look at the data to see if you can recognize any patterns or if there's anything obvious^ such as a few data points that are vastly different from the rest of the set. Plotting data in one: two, or three dimensions can also help. But most of the time you'll have more than three features, and you can't easily plot the data across all features at one time. You could, however use some advanced methods we'll talk about later to distill multiple dimensions down to two or three so you can visualize the data.
4 If you're working with a production system and you know what the data should look like, or you trust its source: you can skip this step. This step takes human involvement, and for an automated system you don't want human involvement. The value of this step is that it makes you understand you don't have garbage coming in.
5 Train the algorithm. This is where the machine learning takes place. This step and the next step are where the "core" algorithms lie, depending on the algorithm.You feed the algorithm good clean data from the first two steps andextract knowledge or information. This knowledge you often store in a formatthat's readily useable by a machine for the next two steps.In the case of unsupervised learning, there's no training step because youdon't have a target value. Everything is used in the next step.
6 Test the algorithm. This is where the information learned in the previous step isput to use. When you're evaluating an algorithm, you'll test it to see how well itdoes. In the case of supervised learning, you have some known values you can use to evaluate the algorithm. In unsupervised learning, you may have to use some other metrics to evaluate the success. In either case, if you're not satisfied, you can go back to step 4, change some things, and try testing again. Often thecollection or preparation of the data may have been the problem, and you'll have to go back to step 1.
7 Use it. Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1-5.
NEW QUESTION 95
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. Linear Regression
- B. Maximum likelihood estimation
- C. Logistic Regression
- D. Hierarchical linear models
- E. Recommendation system
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 96
You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?
- A. Linear Regression
- B. Naive Bayes Classifier
- C. K-means Clustering
- D. Grouping
- E. Association Rules
Answer: C
Explanation:
Explanation
Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.
NEW QUESTION 97
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. Choose the initial centroids so that they are far away from each other
- B. Run at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set
- C. Choose the initial centroids so that they all He along different axes
- D. Only consider values of k larger than log(N), where N is the number of observations in the data set
Answer: A
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 98
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
- A. Discovery
- B. Model Building
- C. Communicate Results
- D. Data Preparation
Answer: D
NEW QUESTION 99
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