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Question: 42
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 . Linear Regression
B . Logistic Regression
C . Discriminant Analysis D . Association Rules
Question: 43
Select the statement which applies correctly to the Naive Bayes A . Works with a small amount of data
B . Sensitive to how the input data is prepared
C . Works with nominal values
Question: 44
A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.
Above is an example of A . Linear Regression B . Logistic Regression
C . Recommendation system
D . Maximum likelihood estimation E . Hierarchical linear models
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 Question: 45
What describes a true limitation of Logistic Regression method? A . It does not handle redundant variables well.
B . It does not handle missing values well.
C . It does not handle correlated variables well. D . It does not have explanatory values.
Question: 46
Which of the following technique can be used to the design of recommender systems? A . Naive Bayes classifier
B . Power iteration
C . Collaborative filtering D . 1 and 3
E . 2 and 3
One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k- nearest neighbor (k-NN) approach and the Pearson Correlation
Question: 47
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 . Numerical B . Categorical
C . Both 1 and 2 are correct
D . None of the 1 and 2 are correct
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.
Question: 48
In unsupervised learning which statements correctly applies A . It does not have a target variable
B . Instead of telling the machine Predict Y for our data X, we’re asking What can you tell me about X? C . telling the machine Predict Y for our data X
In unsupervised learning we don’t have a target variable as we did in classification and regression. Instead of telling the machine Predict Y for our data X, we’re asking What can you tell me about X?
Things we ask the machine to tell us about X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?
Question: 49
You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare
to the honest claims.
Which of the following technique is suitable to find out whether the claim is valid or not? A . Naive Bayes
B . Logistic Regression
C . Random Decision Forests D . Any one of the above
In this problem you have been given high-dimensional independent variables like texts, corrections, 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
Question: 50
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)/P(E2)
B . P(E1+E2)/P(E1)
C . P(E2)/P(E1)
D . P(E2)/(P(E1+E2)
Question: 51
Which of the following statement true with regards to Linear Regression Model? A . Ordinary Least Square can be used to estimates the parameters in linear model
B . In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
C . Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
D . Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
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.
Question: 52
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 remove that 30 people from dataset
D . You will be multiplying standard deviation with the 100
Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.