McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .
| 651. |
SVM is a ------------------ algorithm |
| A. | Classification |
| B. | Clustering |
| C. | Regression |
| D. | All |
| Answer» A. Classification | |
| 652. |
SVM is a ------------------ learning |
| A. | Supervised |
| B. | Unsupervised |
| C. | Both |
| D. | None |
| Answer» A. Supervised | |
| 653. |
The linear SVM classifier works by drawing a straight line between two classes |
| A. | True |
| B. | false |
| Answer» A. True | |
| 654. |
What is Model Selection in Machine Learning? |
| A. | The process of selecting models among different mathematical models, which are used to describe the same data set |
| B. | when a statistical model describes random error or noise instead of underlying relationship |
| C. | Find interesting directions in data and find novel observations/ database cleaning |
| D. | All above |
| Answer» A. The process of selecting models among different mathematical models, which are used to describe the same data set | |
| 655. |
Which are two techniques of Machine Learning ? |
| A. | Genetic Programming and Inductive Learning |
| B. | Speech recognition and Regression |
| C. | Both A & B |
| D. | None of the Mentioned |
| Answer» A. Genetic Programming and Inductive Learning | |
| 656. |
Even if there are no actual supervisors ________ learning is also based on feedback provided by the environment |
| A. | Supervised |
| B. | Reinforcement |
| C. | Unsupervised |
| D. | None of the above |
| Answer» B. Reinforcement | |
| 657. |
When it is necessary to allow the model to develop a generalization ability and avoid a common problem called______. |
| A. | Overfitting |
| B. | Overlearning |
| C. | Classification |
| D. | Regression |
| Answer» A. Overfitting | |
| 658. |
Techniques involve the usage of both labeled and unlabeled data is called___. |
| A. | Supervised |
| B. | Semi-supervised |
| C. | Unsupervised |
| D. | None of the above |
| Answer» B. Semi-supervised | |
| 659. |
A supervised scenario is characterized by the concept of a _____. |
| A. | Programmer |
| B. | Teacher |
| C. | Author |
| D. | Farmer |
| Answer» B. Teacher | |
| 660. |
overlearning causes due to an excessive ______. |
| A. | Capacity |
| B. | Regression |
| C. | Reinforcement |
| D. | Accuracy |
| Answer» A. Capacity | |
| 661. |
Which of the following are several models for feature extraction |
| A. | regression |
| B. | classification |
| C. | None of the above |
| Answer» C. None of the above | |
| 662. |
_____ provides some built-in datasets that can be used for testing purposes. |
| A. | scikit-learn |
| B. | classification |
| C. | regression |
| D. | None of the above |
| Answer» A. scikit-learn | |
| 663. |
While using _____ all labels are turned into sequential numbers. |
| A. | LabelEncoder class |
| B. | LabelBinarizer class |
| C. | DictVectorizer |
| D. | FeatureHasher |
| Answer» A. LabelEncoder class | |
| 664. |
_______produce sparse matrices of real numbers that can be fed into any machine learning model. |
| A. | DictVectorizer |
| B. | FeatureHasher |
| C. | Both A & B |
| D. | None of the Mentioned |
| Answer» C. Both A & B | |
| 665. |
scikit-learn offers the class______, which is responsible for filling the holes using a strategy based on the mean, median, or frequency |
| A. | LabelEncoder |
| B. | LabelBinarizer |
| C. | DictVectorizer |
| D. | Imputer |
| Answer» D. Imputer | |
| 666. |
Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value. |
| A. | MinMaxScaler |
| B. | MaxAbsScaler |
| C. | Both A & B |
| D. | None of the Mentioned |
| Answer» C. Both A & B | |
| 667. |
scikit-learn also provides a class for per-sample normalization,_____ |
| A. | Normalizer |
| B. | Imputer |
| C. | Classifier |
| D. | All above |
| Answer» A. Normalizer | |
| 668. |
______dataset with many features contains information proportional to the independence of all features and their variance. |
| A. | normalized |
| B. | unnormalized |
| C. | Both A & B |
| D. | None of the Mentioned |
| Answer» B. unnormalized | |
| 669. |
In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the_____. |
| A. | Concuttent matrix |
| B. | Convergance matrix |
| C. | Supportive matrix |
| D. | Covariance matrix |
| Answer» D. Covariance matrix | |
| 670. |
The_____ parameter can assume different values which determine how the data matrix is initially processed. |
| A. | run |
| B. | start |
| C. | init |
| D. | stop |
| Answer» C. init | |
| 671. |
______allows exploiting the natural sparsity of data while extracting principal components. |
| A. | SparsePCA |
| B. | KernelPCA |
| C. | SVD |
| D. | init parameter |
| Answer» A. SparsePCA | |
| 672. |
Which of the following statement is true about outliers in Linear regression? |
| A. | Linear regression is sensitive to outliers |
| B. | Linear regression is not sensitive to outliers |
| C. | Can’t say |
| D. | None of these |
| Answer» A. Linear regression is sensitive to outliers | |
| 673. |
Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation? |
| A. | Since the there is a relationship means our model is not good |
| B. | Since the there is a relationship means our model is good |
| C. | Can’t say |
| D. | None of these |
| Answer» A. Since the there is a relationship means our model is not good | |
| 674. |
Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true? |
| A. | You will always have test error zero |
| B. | You can not have test error zero |
| C. | None of the above |
| Answer» C. None of the above | |
| 675. |
In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true? |
| A. | If R Squared increases, this variable is significant. |
| B. | If R Squared decreases, this variable is not significant. |
| C. | Individually R squared cannot tell about variable importance. We can’t say anything about it right now. |
| D. | None of these. |
| Answer» C. Individually R squared cannot tell about variable importance. We can’t say anything about it right now. | |
| 676. |
To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited? |
| A. | Scatter plot |
| B. | Barchart |
| C. | Histograms |
| D. | None of these |
| Answer» A. Scatter plot | |
| 677. |
which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most. |
| A. | The polynomial degree |
| B. | Whether we learn the weights by matrix inversion or gradient descent |
| C. | The use of a constant-term |
| Answer» A. The polynomial degree | |
| 678. |
Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection? |
| A. | Ridge regression uses subset selection of features |
| B. | Lasso regression uses subset selection of features |
| C. | Both use subset selection of features |
| D. | None of above |
| Answer» B. Lasso regression uses subset selection of features | |
| 679. |
Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R-Squared increases and Adjusted R-squared decreases3. R-Squared decreases and Adjusted R-squared decreases4. R-Squared decreases and Adjusted R-squared increases |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 4 |
| D. | None of the above |
| Answer» A. 1 and 2 | |
| 680. |
What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function |
| A. | 1 |
| B. | 2 |
| C. | 1 and 2 |
| D. | None of these |
| Answer» C. 1 and 2 | |
| 681. |
Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very small C (C~0)? |
| A. | Misclassification would happen |
| B. | Data will be correctly classified |
| C. | Can’t say |
| D. | None of these |
| Answer» A. Misclassification would happen | |
| 682. |
The cost parameter in the SVM means: |
| A. | The number of cross-validations to be made |
| B. | The kernel to be used |
| C. | The tradeoff between misclassification and simplicity of the model |
| D. | None of the above |
| Answer» C. The tradeoff between misclassification and simplicity of the model | |
| 683. |
How do you handle missing or corrupted data in a dataset? |
| A. | a. Drop missing rows or columns |
| B. | b. Replace missing values with mean/median/mode |
| C. | c. Assign a unique category to missing values |
| D. | d. All of the above |
| Answer» D. d. All of the above | |
| 684. |
Which of the following statements about Naive Bayes is incorrect? |
| A. | Attributes are equally important. |
| B. | Attributes are statistically dependent of one another given the class value. |
| C. | Attributes are statistically independent of one another given the class value. |
| D. | Attributes can be nominal or numeric |
| Answer» B. Attributes are statistically dependent of one another given the class value. | |
| 685. |
The SVM’s are less effective when: |
| A. | The data is linearly separable |
| B. | The data is clean and ready to use |
| C. | The data is noisy and contains overlapping points |
| Answer» C. The data is noisy and contains overlapping points | |
| 686. |
If there is only a discrete number of possible outcomes called _____. |
| A. | Modelfree |
| B. | Categories |
| C. | Prediction |
| D. | None of above |
| Answer» B. Categories | |
| 687. |
Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic. |
| A. | Inference |
| B. | Interference |
| C. | Accuracy |
| D. | None of above |
| Answer» A. Inference | |
| 688. |
The term _____ can be freely used, but with the same meaning adopted in physics or system theory. |
| A. | Accuracy |
| B. | Cluster |
| C. | Regression |
| D. | Prediction |
| Answer» D. Prediction | |
| 689. |
Common deep learning applications / problems can also be solved using____ |
| A. | Real-time visual object identification |
| B. | Classic approaches |
| C. | Automatic labeling |
| D. | Bio-inspired adaptive systems |
| Answer» B. Classic approaches | |
| 690. |
what is the function of ‘Unsupervised Learning’? |
| A. | Find clusters of the data and find low-dimensional representations of the data |
| B. | Find interesting directions in data and find novel observations/ database cleaning |
| C. | Interesting coordinates and correlations |
| D. | All |
| Answer» D. All | |
| 691. |
What are the two methods used for the calibration in Supervised Learning? |
| A. | Platt Calibration and Isotonic Regression |
| B. | Statistics and Informal Retrieval |
| Answer» A. Platt Calibration and Isotonic Regression | |
| 692. |
Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct? |
| A. | It is more likely for X1 to be excluded from the model |
| B. | It is more likely for X1 to be included in the model |
| C. | Can’t say |
| D. | None of these |
| Answer» B. It is more likely for X1 to be included in the model | |
| 693. |
Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection? |
| A. | Ridge regression uses subset selection of features |
| B. | Lasso regression uses subset selection of features |
| C. | Both use subset selection of features |
| D. | None of above |
| Answer» B. Lasso regression uses subset selection of features | |
| 694. |
Which of the following statement(s) can be true post adding a variable in a linear regression model?
|
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 4 |
| D. | None of the above |
| Answer» A. 1 and 2 | |
| 695. |
We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Which of the following is/are true about “Normal Equation”?
|
| A. | 1 and 2 |
| B. | 1 and 3. |
| C. | 2 and 3. |
| D. | 1,2 and 3. |
| Answer» D. 1,2 and 3. | |
| 696. |
If two variables are correlated, is it necessary that they have a linear relationship? |
| A. | Yes |
| B. | No |
| Answer» B. No | |
| 697. |
When the C parameter is set to infinite, which of the following holds true? |
| A. | The optimal hyperplane if exists, will be the one that completely separates the data |
| B. | The soft-margin classifier will separate the data |
| C. | None of the above |
| Answer» A. The optimal hyperplane if exists, will be the one that completely separates the data | |
| 698. |
Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very large value of C(C->infinity)? |
| A. | We can still classify data correctly for given setting of hyper parameter C |
| B. | We can not classify data correctly for given setting of hyper parameter C |
| C. | Can’t Say |
| D. | None of these |
| Answer» A. We can still classify data correctly for given setting of hyper parameter C | |
| 699. |
SVM can solve linear and non-linear problems |
| A. | true |
| B. | false |
| Answer» A. true | |
| 700. |
The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points. |
| A. | true |
| B. | false |
| Answer» A. true | |
Done Studing? Take A Test.
Great job completing your study session! Now it's time to put your knowledge to the test. Challenge yourself, see how much you've learned, and identify areas for improvement. Don’t worry, this is all part of the journey to mastery. Ready for the next step? Take a quiz to solidify what you've just studied.