McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .
| 351. |
In reinforcement learning if feedback is negative one it is defined as . |
| A. | penalty |
| B. | overlearning |
| C. | reward |
| D. | none of above |
| Answer» A. penalty | |
| 352. |
According to , it's a key success factor for the survival and evolution of all species. |
| A. | claude shannon\s theory |
| B. | gini index |
| C. | darwin's theory |
| D. | none of above |
| Answer» C. darwin's theory | |
| 353. |
A supervised scenario is characterized by the concept of a . |
| A. | programmer |
| B. | teacher |
| C. | author |
| D. | farmer |
| Answer» B. teacher | |
| 354. |
overlearning causes due to an excessive . |
| A. | capacity |
| B. | regression |
| C. | reinforcement |
| D. | accuracy |
| Answer» A. capacity | |
| 355. |
Which of the following is an example of a deterministic algorithm? |
| A. | pca |
| B. | k-means |
| C. | none of the above |
| Answer» A. pca | |
| 356. |
Which of the following model model include a backwards elimination feature selection routine? |
| A. | mcv |
| B. | mars |
| C. | mcrs |
| D. | all above |
| Answer» B. mars | |
| 357. |
Can we extract knowledge without apply feature selection |
| A. | yes |
| B. | no |
| Answer» A. yes | |
| 358. |
While using feature selection on the data, is the number of features decreases. |
| A. | no |
| B. | yes |
| Answer» B. yes | |
| 359. |
Which of the following are several models |
| A. | regression |
| B. | classification |
| C. | none of the above |
| Answer» C. none of the above | |
| 360. |
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 | |
| 361. |
While using all labels are turned into sequential numbers. |
| A. | labelencoder class |
| B. | labelbinarizer class |
| C. | dictvectorizer |
| D. | featurehasher |
| Answer» A. labelencoder class | |
| 362. |
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 | |
| 363. |
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 | |
| 364. |
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 | |
| 365. |
scikit-learn also provides a class for per- sample normalization, |
| A. | normalizer |
| B. | imputer |
| C. | classifier |
| D. | all above |
| Answer» A. normalizer | |
| 366. |
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 | |
| 367. |
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 | |
| 368. |
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 | |
| 369. |
allows exploiting the natural sparsity of data while extracting principal components. |
| A. | sparsepca |
| B. | kernelpca |
| C. | svd |
| D. | init parameter |
| Answer» A. sparsepca | |
| 370. |
Which of the following is true about Residuals ? |
| A. | lower is better |
| B. | higher is better |
| C. | a or b depend on the situation |
| D. | none of these |
| Answer» A. lower is better | |
| 371. |
Overfitting is more likely when you have huge amount of data to train? |
| A. | true |
| B. | false |
| Answer» B. false | |
| 372. |
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 | |
| 373. |
Lets 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 | |
| 374. |
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. | |
| 375. |
Which of the one is true about Heteroskedasticity? |
| A. | linear regression with varying error terms |
| B. | linear regression with constant error terms |
| C. | linear regression with zero error terms |
| D. | none of these |
| Answer» A. linear regression with varying error terms | |
| 376. |
Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free |
| A. | 1,2 and 3. |
| B. | 1,3 and 4. |
| C. | 1 and 3. |
| D. | all of above. |
| Answer» D. all of above. | |
| 377. |
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 | |
| 378. |
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 | |
| 379. |
Can we calculate the skewness of variables based on mean and median? |
| A. | true |
| B. | false |
| Answer» B. false | |
| 380. |
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 | |
| 381. |
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- |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 4 |
| D. | none of the above |
| Answer» A. 1 and 2 | |
| 382. |
How many coefficients do you need to estimate in a simple linear regression model (One independent variable)? |
| A. | 1 |
| B. | 2 |
| C. | can't say |
| Answer» B. 2 | |
| 383. |
Conditional probability is a measure of the probability of an event given that another event has already occurred. |
| A. | true |
| B. | false |
| Answer» A. true | |
| 384. |
What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function |
| A. | 1 |
| B. | 2 |
| C. | 1 and 2 |
| D. | none of these |
| Answer» C. 1 and 2 | |
| 385. |
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 its 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 | |
| 386. |
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 | |
| 387. |
If you remove the non-red circled points from the data, the decision boundary will |
| A. | true |
| B. | false |
| Answer» B. false | |
| 388. |
How do you handle missing or corrupted data in a dataset? |
| A. | drop missing rows or columns |
| B. | replace missing values with mean/median/mode |
| C. | assign a unique category to missing values |
| D. | all of the above |
| Answer» D. all of the above | |
| 389. |
The SVMs 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 | |
| 390. |
If there is only a discrete number of possible outcomes called . |
| A. | modelfree |
| B. | categories |
| C. | prediction |
| D. | none of above |
| Answer» B. categories | |
| 391. |
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 | |
| 392. |
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 | |
| 393. |
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 | |
| 394. |
Identify the various approaches for machine learning. |
| A. | concept vs classification learning |
| B. | symbolic vs statistical learning |
| C. | inductive vs analytical learning |
| D. | all above |
| Answer» D. all above | |
| 395. |
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 | |
| 396. |
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 | |
| 397. |
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 | |
| 398. |
Let's say, a Linear regression model perfectly fits the training data (train error |
| 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 | |
| 399. |
Which of the following assumptions do we make while deriving linear regression parameters?
|
| A. | 1,2 and 3. |
| B. | 1,3 and 4. |
| C. | 1 and 3. |
| D. | all of above. |
| Answer» D. all of above. | |
| 400. |
Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). 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 | |
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