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NEW QUESTION 67
What type of output generated in case of linear regression?
- A. Continuous variable
- B. Any of the Continuous and Discrete variable
- C. Values between 0 and 1
- D. Discrete Variable
Answer: A
Explanation:
Explanation
Linear regression model generate continuous output variable.
NEW QUESTION 68
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 penalty term is not differentiate
- B. The objective function is not convex
- C. The second derivative is not constant
- D. The constraints are quadratic
Answer: A
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 69
RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a______, as it is scale-dependent.
- A. Between Variables
- B. Particular Variable
- C. All of the above are correct
- D. Among all the 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 70
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 71
Which of the following is a correct example of the target variable in regression (supervised learning)?
- A. Reptile, fish, mammal, amphibian, plant, fungi
- B. All of the above
- C. Nominal values like true, false
- D. Infinite number of numeric values, such as 0.100, 42.001, 1000.743..
Answer: B
Explanation:
Explanation
We address two cases of the target variable. The first case occurs when the target variable can take only nominal values: true or false; reptile, fish: mammal, amphibian, plant, fungi. The second case of classification occurs when the target variable can take an infinite number of numeric values, such as 0.100, 42.001,
1000.743, .... This case is called regression.
NEW QUESTION 72
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 73
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 74
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 75
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 76
What are the key outcomes of the successful analytical projects?
- A. Code of the model
- B. Technical specifications
- C. Presentations for the Analysts
- D. Presentation for Project Sponsors
Answer: A,B,C,D
Explanation:
Explanation
When your analytical project successfully completed they come up with the following at the end of the projects. Presentations- You will be having presentations like for the all the stakeholders, generally these presentation will help seniors executives to make better decisions. Similarly you would be creating presentations for the other teams like analysts various visuals you would be creating like ROC Curves, Heat Maps, and Bar Charts etc.
Whatever tools you have used like SAS, R, or Python then accordingly code was developed and you will get that code as one of the outcome. Also you would have created a technical specifications for implementing the codes.
NEW QUESTION 77
In which lifecycle stage are appropriate analytical techniques determined?
- A. Model building
- B. Model planning
- C. Data preparation
- D. Discovery
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 78
Which is an example of supervised learning?
- A. SVD
- B. k-means clustering
- C. EM
- D. SVM
- E. PCA
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 79
Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?
- A. It reduces the non-significant features e.g. punctuations
- B. Noisy features are removed
- C. It creates the smaller models
- D. It requires the lesser memory to store the coefficients for the model
Answer: D
Explanation:
Explanation
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.
The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
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. un-supervised learning
- B. supervised learning
- C. Support vector machines
- D. k-Nearest Neighbors
Answer: A
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
In unsupervised learning which statements correctly applies
- A. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
- B. telling the machine Predict Y for our data X
- C. It does not have a target variable
Answer: A,C
Explanation:
Explanation
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?
NEW QUESTION 82
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?
- A. The data is unformatted.
- B. There are missing values in the data.
- C. There is not enough data to create a test set.
- D. There are categorical variables in the model.
Answer: C
NEW QUESTION 83
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 is the same
- B. None of the above
- C. In both cases, the contribution to the RMSE is the different
- D. In both cases, the contribution to the RMSE, could varies
Answer: A
NEW QUESTION 84
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
- A. Communicate Results
- B. Model Building
- C. Data Preparation
- D. Discovery
Answer: C
NEW QUESTION 85
What are the advantages of the Hashing Features?
- A. Easily reverse engineer vectors to determine which original feature mapped to a vector location
- B. Less pass through the training data
- C. Requires the less memory
Answer: B,C
Explanation:
Explanation
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
NEW QUESTION 86
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 87
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of "pink" items in the test set of color balls?
- A. 4.8
- B. 24 0
- C. .24
- D. 2.4
- E. .48
Answer: C
Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency in the training set. The method of maximum likelihood corresponds to many well-known estimation methods in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to measure the height of every single penguin in a population due to cost or time constraints. Assuming that the heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE would accomplish this by taking the mean and variance as parameters and finding particular parametric values that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the "agreement" of the selected model with the observed data, and for discrete random variables it indeed maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems. However in some complicated problems, difficulties do occur: in such problems, maximum-likelihood estimators are unsuitable or do not exist.
NEW QUESTION 88
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?
- A. Linear regression
- B. Decision trees
- C. TF-IDF
- D. Logistic regression
Answer: A
Explanation:
Explanation : A data model explicitly describes a relationship between predictor and response variables.
Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models.
Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation.
If you need to fit data with a nonlinear model, transform the variables to make the relationship linear.
Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.
79
NEW QUESTION 89
You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?
- A. k-nearest neighbors, because it uses local neighborhoods to classify examples
- B. Naive Bayes, because Bayesian methods act as regularlizers
- C. Logistic regression with L1 regularization, to prevent overfitting
- D. Random forest because it is an ensemble method
Answer: C
Explanation:
Explanation
Logistic regression is widely used in machine learning for classification problems. It is well-known that regularization is required to avoid over-fitting, especially when there is a only small number of training examples, or when there are a large number of parameters to be learned. In particular L1 regularized logistic regression is often used for feature selection, and has been shown to have good generalization performance in the presence of many irrelevant features. (Ng 2004; Goodman 2004) Unregularized logistic regression is an unconstrained convex optimization problem with a continuously differentiate objective function. As a consequence, it can be solved fairly efficiently with standard convex optimization methods, such as Newton's method or conjugate gradient. However, adding the L1 regularization makes the optimization problem com-putationally more expensive to solve. If the L1 regulariza-tion is enforced by an L1 norm constraint on the parameLogistic regression is a classifier and L1 regularization tends to produce models that ignore dimensions of the input that are not predictive. This is particularly useful when the input contains many dimensions, k-nearest neighbors classification is also a classification technique, but relies on notions of distance. In a high-dimensional space, most every data point is "far" from others (the curse of dimensionality) and so these techniques break down. Naive Bayes is not inherently regularizing. Random forests represent an ensemble method; but an ensemble method is not necessarily more suitable to high-dimensional data.
Practically, I think the biggest reasons for regularization are 1) to avoid overfitting by not generating high coefficients for predictors that are sparse. 2) to stabilize the estimates especially when there's collinearity in the data.
1) is inherent in the regularization framework. Since there are two forces pulling each other in the objective function, if there's no meaningful loss reduction, the increased penalty from the regularization term wouldn't improve the overall objective function. This is a great property since a lot of noise would be automatically filtered out from the model. To give you an example for 2), if you have two predictors that have same values, if you just run a regression algorithm on it since the data matrix is singular your beta coefficients will be Inf if you try to do a straight matrix inversion. But if you add a very small regularization lambda to it, you will get stable beta coefficients with the coefficient values evenly divided between the equivalent two variables. For the difference between L1 and L2, the following graph demonstrates why people bother to have L1 since L2 has such an elegant analytical solution and is so computationally straightforward. Regularized regression can also be represented as a constrained regression problem (since they are Lagrangian equivalent). The implication of this is that the L1 regularization gives you sparse estimates. Namely, in a high dimensional space, you got mostly zeros and a small number of non-zero coefficients. This is huge since it incorporates variable selection to the modeling problem. In addition, if you have to score a large sample with your model, you can have a lot of computational savings since you don't have to compute features(predictors) whose coefficient is 0. I personally think L1 regularization is one of the most beautiful things in machine learning and convex optimization. It is indeed widely used in bioinformatics and large scale machine learning for companies like Facebook, Yahoo, Google and Microsoft.
NEW QUESTION 90
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