Introduction To Machine Learning MCQs with answers | letsbug

MCQs for intro to Machine Learning

1. _______ is about developing code to enable the machine to learn to perform tasks and its basic principle is the automatic modeling of underlying processes that have generated the collected data.

1. Data Science
2. Machine Learning(ML)
3. Data Analytics
4. All of the above

Answer: 2 : machine Learning(ML)

2. Groups of related observations are called _______ and the procedure to organize items of given collection into groups based on some similar features called as _____.

1. culusters, analysis
2. regression, clustering
3. clusters, clustering
4. none of the above

Answer: 3 : clusters, clustering 

3. ____ rule mining is a technique to identify underlying relations between different items. 

1. Association 
2. Analytics
3. Learning
4. All of the above

Answer: 1 : Association

4. _______ is a form of supervised learning. Mail service providers like Gmail, Yahoo and others use this technique to classify a new mail as spam or not spam.

1. Machine Learning
2. Classification
3. Regression 
4. All of the above

Answer: 2: Classification

5. Examples of supervised learning includes _______ .

1. Regression
2. Decision Tree
3. KNN
4. All of the above    

Answer: 4 : all of the above 

6. A Naive Bayes Classifier is a ______ machine learning algorithm which relies on the assumption of features independent to classify input data.

1. supervised
2. unsupervised
3. semi-supervised
4. all of the above

Answer: 1: supervised

7. ______ analysis has become one of the most widely used statistical tools for analyzing  multi factor data and it is appealing because it provides a conceptually simple method for investigating functional relationships among variables.

1. Clustering 
2. Regression
3. Data
4. All of the above

Answer: 2 : Regression.

8.  A ________ is an example of the most widely used machine learning algorithms much of its popularity is because it can be adapted to almost any type of data.

1. Clustering
2. Regression
3. Decision Tree
4. All of the above

Answer: 3: Decision Tree

9. Unsupervised learning makes sense of ________ data without having any predefined dataset for its training. 

1. unlabeled
2. labeled
3. semi-labeled
4. all of the above

Answer: 1 : unlabeled

10. Support Vector Machines (SVMs) are well-known _______ classification algorithms that separate different categories of data.

1. Semi-supervised
2. unsupervised
3. supervised 
4. all of the above

Answer: 3:  supervised

11.  ______ are some of the examples of unsupervised learning 

1. Apriori Algorithm 
2. K-means
3. Cluster analysis
4. All of the above

Answer: 4 : all of the above

12. _________ is an algorithm for the frequent item set mining and association rule learning over transactional databases and it proceeds by identifying the frequent individual items in the database and extending them to larger and larger items sets as long as those items appear sufficiently often in the database.

1. Apriori 
2. K-means
3. KNN
4. all of the above

Answer: 1 : Apriori

13. The ____ algorithm is the simplest machine learning algorithm, which building the the model consists only of storing the training dataset. To make a prediction of a new data point. the algorithm finds the closest data points in the training dataset-its nearest neighbors

1. Apriori
2. K-means
3. KNN
4. all of the above

Answer: 3 : KNN

14. Association rule mining discovers strong _______ relationships among data.

1. Association
2. correlation
3. Both (2) and (1)
4. None of the above

Answer: 3: both (2) and (1)