Machine Learning Multiple Choice Questions with Answers 29

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, lazy learner, k-nearest neighbor, eager learner, SVM classifier

 

Machine learning Quiz Questions - Set 29

 

1. Which of the following is a lazy learner?

a) k-nearest neighbor

b) Linear regression

c) Support Vector Machine

d) Artificial Neural Network

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Ans : (a)

Answer: (a) k-nearest neighbor k-NN is a lazy learner because it doesn’t learn a discriminative function from the training data but “memorizes” the training dataset instead. There is no training time in k-NN. Due to this, the prediction step is expensive. k-NN does not generalize the input training data. What is lazy learner? In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries.

 

2. Which of the following is a disadvantage of k-Nearest Neighbors algorithm?

a) Low accuracy

b) Insensitive to outliers

c) Computationally expensive

d) Need very less memory

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Ans : (c)

Answer: (c) Computationally expensive Due to the absence of training time, k-NN has to involve the entire dataset during the prediction step. This particular property makes k-NN a computationally expensive classification algorithm.

 

3. Which of the following is an eager learner?

a) k-nearest neighbor

b) Support Vector Machine

c) case-based reasoning

d) None of the above

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Ans : (b)

Answer: (b) Support Vector Machine SVM is a classification method for classifying both linear and non-linear data. SVM builds a hyperplane classification model during training. It finds this hyperplane using support vectors (“essential” training tuples) and margins (defined by the support vectors). What is eager learner? Eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system. Eager learners, when given a set of training tuples, will  construct  a  generalization  (i.e.,  classification)  model before  receiving new  e.g., test)  tuples  to  classify.

 

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Machine Learning TRUE or FALSE Questions with Answers 20

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Machine Learning TRUE / FALSE Questions - SET 20

1. Training neural networks has the potential problem of over-fitting the training data.

(a) TRUE                                                   (b) FALSE

Answer: TRUE Over-fitting occurs when a model tries to predict a trend in data that is too noisy. This is caused due to an overly complex model with too many parameters. A model that is over-fit is inaccurate because the trend does not reflect the reality present in the data. Many of the modern advancements in neural networks have been a result of stacking many hidden layers. This deep stacking allows us to learn more complex relationships in the data. However, because we're increasing the complexity of the model, we're also more prone to potentially over-fitting our data. References – first, second.

 

2. Given a linearly-separable dataset for a 2-class classification problem, a Linear SVM is better to use than a Perceptron because the SVM will often be able to achieve better classification accuracy on the testing set.

(a) TRUE                                                   (b) FALSE

Answer: TRUE Perceptron stops after it classifies data correctly whereas SVM stops after finding the best plane that has the maximum margin, i.e. the maximum distance between data points of both classes. Maximizing the margin distance provides some reinforcement so that future data points can be classified with more confidence. The decision boundary learned by the SVM will often be farther away from more of the training examples, leading to better performance on the testing examples that are close to the decision boundary.

 

3. With a non-linearly-separable dataset that contains some extra noise, using an SVM with slack variables to create a soft margin classifier, and a small value for the penalty parameter, C, that controls how much to penalize misclassified points, will often reduce over-fitting the training data.

(a) TRUE                                                   (b) FALSE

Answer: TRUE Small C means the penalty for misclassifying a few points will be small and therefore we are more likely to maximize the margin between most of the points while misclassifying a few points including the noise points.

 

4. Hierarchical Agglomerative Clustering requires the user to specify the number of clusters, k, when building the dendrogram for a set of data.

(a) TRUE                                                   (b) FALSE

Answer: FALSE Hierarchical agglomerative clustering algorithm uses “bottom-up” approach. It begins with each object in a separate cluster. At each step, the two clusters that are most similar are joined into a single new cluster. Once fused, objects are never separated. These techniques do not let you explicitly set the number of clusters. Instead, you pick a distance value that will yield an appropriate number of clusters.

 

5. A good way to pick the number of clusters, k, used for k-Means clustering is to try multiple values of k and choose the value that minimizes the distortion measure.

(a) TRUE                                                   (b) FALSE

Answer: TRUE Choosing an optimal k value is a hard problem. It is costly to choose the k value by trying multiple different values of k. Rather, the value for k can be chosen using these methods; Elbow curve method, silhouette analysis, gap statistic.  The most popular among these is elbow method.

 

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Machine Learning Multiple Choice Questions with Answers 28

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Machine learning Quiz Questions - Set 28

 

1. Which of the following is true about generative models?

a) They capture the joint probability

b) The perceptron is a generative model

c) Generative models can be used for classification

d) They capture the conditional probability

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Ans : (a) and (c)

Answer: (a) They capture the joint probability and (c) Generative models can be used for classification Generative models are useful for unsupervised learning tasks. A generative model learns parameters by maximizing the joint probability P(X,Y). Generative models encode full probability distributions and specify how to generate data that fit such distributions. Bayesian networks are well-known examples of such models. Refer here for more information.   Generative Classifiers tries to model class, i.e., what are the features of the class. In short, it models how a particular class would generate input data. When a new observation is given to these classifiers, it tries to predict which class would have most likely generated the given observation. Refer here for more information.

 

2. Which of the following are true about subset selection?

a) Subset selection can substantially decrease the bias of support vector machines

b) Ridge regression frequently eliminates some of the features

c) Finding the true best subset takes exponential time

d) Subset selection can reduce overfitting

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Ans : (d)

Answer: (d) Subset selection can reduce overfitting A classifier is said to overfit to a dataset if it models the training data too closely and gives poor predictions on new data. This occurs when there is insufficient data to train the classifier and the data does not fully cover the concept being learned. Subset selection reduces over-fitting. Feature subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. This reduces the dimensionality of the data and allows learning algorithms to operate faster and more effectively.

 

3. What can help to reduce overfitting in an SVM classifier?

a) High-degree polynomial features

b) Setting a very low learning rate

c) Use of slack variables

d) Normalizing the data

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Ans : (c)

Answer: (c) Use of slack variables The reason that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations, is that it uses regularization. The key to avoid over-fitting lies in careful tuning of the regularization parameter, C, and in the case of non-linear SVMs, careful choice of kernel and tuning of the kernel parameters. Without slack variables the SVM would be forced into always fitting the data exactly and would often overfit as a result.

 

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