Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable a system to improve its performance on a specific task over time. In other words, machine learning algorithms are designed to allow a computer to learn from data, without being explicitly programmed.
| 1. |
Application of machine learning methods to large databases is called |
| A. | data mining. |
| B. | artificial intelligence |
| C. | big data computing |
| D. | internet of things |
| Answer» A. data mining. | |
| 2. |
If machine learning model output involves target variable then that model is called as |
| A. | descriptive model |
| B. | predictive model |
| C. | reinforcement learning |
| D. | all of the above |
| Answer» B. predictive model | |
| 3. |
In what type of learning labelled training data is used |
| A. | unsupervised learning |
| B. | supervised learning |
| C. | reinforcement learning |
| D. | active learning |
| Answer» B. supervised learning | |
| 4. |
In following type of feature selection method we start with empty feature set |
| A. | forward feature selection |
| B. | backword feature selection |
| C. | both a and b?? |
| D. | none of the above |
| Answer» A. forward feature selection | |
| 5. |
In PCA the number of input dimensiona are equal to principal components |
| A. | true |
| B. | false |
| Answer» A. true | |
| 6. |
PCA can be used for projecting and visualizing data in lower dimensions. |
| A. | true |
| B. | false |
| Answer» A. true | |
| 7. |
Which of the following is the best machine learning method? |
| A. | scalable |
| B. | accuracy |
| C. | fast |
| D. | all of the above |
| Answer» D. all of the above | |
| 8. |
What characterize unlabeled examples in machine learning |
| A. | there is no prior knowledge |
| B. | there is no confusing knowledge |
| C. | there is prior knowledge |
| D. | there is plenty of confusing knowledge |
| Answer» D. there is plenty of confusing knowledge | |
| 9. |
What does dimensionality reduction reduce? |
| A. | stochastics |
| B. | collinerity |
| C. | performance |
| D. | entropy |
| Answer» B. collinerity | |
| 10. |
Data used to build a data mining model. |
| A. | training data |
| B. | validation data |
| C. | test data |
| D. | hidden data |
| Answer» A. training data | |
| 11. |
The problem of finding hidden structure in unlabeled data is called… |
| A. | supervised learning |
| B. | unsupervised learning |
| C. | reinforcement learning |
| D. | none of the above |
| Answer» B. unsupervised learning | |
| 12. |
Of the Following Examples, Which would you address using an supervised learning Algorithm? |
| A. | given email labeled as spam or not spam, learn a spam filter |
| B. | given a set of news articles found on the web, group them into set of articles about the same story. |
| C. | given a database of customer data, automatically discover market segments and group customers into different market segments. |
| D. | find the patterns in market basket analysis |
| Answer» A. given email labeled as spam or not spam, learn a spam filter | |
| 13. |
Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model |
| A. | true |
| B. | false |
| Answer» A. true | |
| 14. |
You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of |
| A. | supervised learning |
| B. | unsupervised learning |
| C. | semisupervised learning |
| D. | reinforcement learning |
| Answer» A. supervised learning | |
| 15. |
Which of the following is a good test dataset characteristic? |
| A. | large enough to yield meaningful results |
| B. | is representative of the dataset as a whole |
| C. | both a and b |
| D. | none of the above |
| Answer» C. both a and b | |
| 16. |
Following are the types of supervised learning |
| A. | classification |
| B. | regression |
| C. | subgroup discovery |
| D. | all of the above |
| Answer» D. all of the above | |
| 17. |
Type of matrix decomposition model is |
| A. | descriptive model |
| B. | predictive model |
| C. | logical model |
| D. | none of the above |
| Answer» A. descriptive model | |
| 18. |
Following is powerful distance metrics used by Geometric model |
| A. | euclidean distance |
| B. | manhattan distance |
| C. | both a and b?? |
| D. | square distance |
| Answer» C. both a and b?? | |
| 19. |
The output of training process in machine learning is |
| A. | machine learning model |
| B. | machine learning algorithm |
| C. | null |
| D. | accuracy |
| Answer» A. machine learning model | |
| 20. |
A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
|
| A. | nominal |
| B. | ordinal |
| C. | categorical |
| D. | boolean |
| Answer» B. ordinal | |
| 21. |
PCA is |
| A. | forward feature selection |
| B. | backword feature selection |
| C. | feature extraction |
| D. | all of the above |
| Answer» C. feature extraction | |
| 22. |
Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model. |
| A. | true |
| B. | false |
| Answer» A. true | |
| 23. |
Which of the following techniques would perform better for reducing dimensions of a data set? |
| A. | removing columns which have too many missing values |
| B. | removing columns which have high variance in data |
| C. | removing columns with dissimilar data trends |
| D. | none of these |
| Answer» A. removing columns which have too many missing values | |
| 24. |
Supervised learning and unsupervised clustering both require which is correct according to the statement. |
| A. | output attribute. |
| B. | hidden attribute. |
| C. | input attribute. |
| D. | categorical attribute |
| Answer» C. input attribute. | |
| 25. |
What characterize is hyperplance in geometrical model of machine learning? |
| A. | a plane with 1 dimensional fewer than number of input attributes |
| B. | a plane with 2 dimensional fewer than number of input attributes |
| C. | a plane with 1 dimensional more than number of input attributes |
| D. | a plane with 2 dimensional more than number of input attributes |
| Answer» B. a plane with 2 dimensional fewer than number of input attributes | |