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NEW QUESTION 31
RMSE measures error of a predicted
- A. For booth Numerical and categorical values
- B. Numerical Value
- C. Categorical values
Answer: B
NEW QUESTION 32
Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B.Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B.40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?
- A. Binomial Distribution
- B. None of the above
- C. Bays Theorem
- D. Poisson Distribution
Answer: C
NEW QUESTION 33
Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because
- A. The objective function is not convex
- B. The penalty term is not differentiate
- C. The constraints are quadratic
- D. The second derivative is not constant
Answer: B
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.
Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum.
You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.
NEW QUESTION 34
RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a______, as it is scale-dependent.
- A. All of the above are correct
- B. Particular Variable
- C. Among all the variables
- D. Between Variables
Answer: B
Explanation:
Explanation : The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.
NEW QUESTION 35
Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything about this conversation, the probability that he was speaking to a woman is 50% (assuming the train had an equal number of men and women and the speaker was as likely to strike up a conversation with a man as with a woman). Now suppose he also told you that his conversational partner had long hair. It is now more likely he was speaking to a woman, since women are more likely to have long hair than men.____________ can be used to calculate the probability that the person was a woman.
- A. SVM
- B. MLE
- C. Bayes' theorem
- D. Logistic Regression
Answer: C
Explanation:
Explanation
To see how this is done, let W represent the event that the conversation was held with a woman, and L denote the event that the conversation was held with a long*haired person. It can be assumed that women constitute half the population for this example. So, not knowing anything else, the probability that W occurs is P(W) =
0.5. Suppose it is also known that 75% of women have long hair which we denote as P(L |W) = 0.75 (read: the probability of event L given event W is 0.75, meaning that the probability of a person having long hair (event
"L"): given that we already know that the person is a woman ("event W") is 75%). Likewise, suppose it is known that 15% of men have long hair, or P(L |M) = 0.15; where M is the complementary event of W: i.e.; the event that the conversation was held with a man (assuming that every human is either a man or a woman).
Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, P(W |L). Using the formula for Bayes' theorem, we have:
Text Description automatically generated with low confidence
where we have used the law of total probability to expand
P(L),
The numeric answer can be obtained by substituting the above values into this formula (the algebraic multiplication is annotated using " *", the centered dot). This yields A picture containing table Description automatically generated
i.e., the probability that the conversation was held with a woman, given that the person had long hair is about
83%. More examples are provided below.
NEW QUESTION 36
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. 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.
- C. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
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 37
Select the correct statement regarding the naive Bayes classification
- A. it only requires a small amount of training data to estimate the parameters
- B. only the variances of the variables for each class need to be determined
- C. Independent variables can be assumed
- D. for each class entire covariance matrix need to be determined
Answer: A,B,C
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 38
You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?
- A. You will take square of the height.
- B. You will take square root of weight
- C. You will take cube root of height
- D. You would consider using BMI (Body Mass Index)
Answer: D
Explanation:
Explanation
If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)
NEW QUESTION 39
Which of the following statement true with regards to Linear Regression Model?
- A. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
- B. Ordinary Least Square can be used to estimates the parameters in linear model
- C. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
- D. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
Answer: B,C
Explanation:
Explanation
Linear regression model are represented using the below equation
Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1).
And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.
NEW QUESTION 40
A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?
- A. Association Rules
- B. Naive Bayesian
- C. Logistic Regression
- D. K Means Clustering
Answer: D
Explanation:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
NEW QUESTION 41
In which of the following scenario you should apply the Bay's Theorem
- A. In all above cases
- B. Within the sample space, there exists an event B, for which P(B) > 0.
- C. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
- D. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
Answer: A
NEW QUESTION 42
Which of the following statement is true for the R square value in the regression model?
- A. When R square =1 , all the residuals are equal to 0
- B. When R square =0, all the residual are equal to 1
- C. R square can be increased by adding more variables to the model.
- D. R-squared never decreases upon adding more independent variables.
Answer: A,C,D
NEW QUESTION 43
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(E2)/P(E1)
- B. P(E1)/P(E2)
- C. P(E2)/(P(E1+E2)
- D. P(E1+E2)/P(E1)
Answer: A
NEW QUESTION 44
You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?
- A. You can combine several variables in one variable
- B. You will randomly reduce the number of variables
- C. You cannot discard any variable for creating clusters.
- D. You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.
- E. You will find the correlation among the variables and from their variables are not co-related will be discarded.
Answer: A,D
Explanation:
Explanation
When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation.
You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.
NEW QUESTION 45
Select the correct statement which applies to Principal component analysis (PCA)
- A. 1 and 3 are correct
- B. 1 and 2 are correct
- C. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.
- D. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables
- E. Increase the dimensionality of the data set.
Answer: C
Explanation:
Explanation
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrected variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
NEW QUESTION 46
Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?
- A. Support Vector Count
- B. Cluster Density
- C. Sum of Absolute Errors
- D. Mean Absolute Error
Answer: D
Explanation:
Explanation
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references.
Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.
Support vector count and cluster density do not apply to recommender systems.
MAE and AUC are both valid and useful metrics for measuring recommender systems.
NEW QUESTION 47
Select the correct statement which applies to logistic regression
- A. May have low accuracy
- B. Works with Numeric values
- C. Computationally inexpensive, easy to implement knowledge representation easy to interpret
Answer: A,B,C
NEW QUESTION 48
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. Logistic Regression
- B. Association Rules
- C. Discriminant Analysis
- D. Linear Regression
Answer: A
NEW QUESTION 49
Find out the classifier which assumes independence among all its features?
- A. Naive Bayes
- B. Random forests
- C. Neural networks
- D. Linear Regression
Answer: A
Explanation:
Explanation
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.
NEW QUESTION 50
Which of the following problem you can solve using binomial distribution
- A. 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?
- B. 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 - C. 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
- D. 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.
Answer: A
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 51
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