Statistics for Data Science & Analytics

The quiz is about Machine Learning MCQs Questions with Answers. Test your Machine Learning knowledge with this 20-question MCQ quiz! Perfect for students, data analysts, data scientists, and statisticians, this Machine Learning MCQs Questions Quiz covers key concepts like Naive Bayes, K-means, decision trees, random forests, and ensemble learning. Sharpen your skills and assess your understanding of supervised and unsupervised learning techniques in an academic and professional context. Let us start with the Machine Learning MCQs Questions with Answers now.

Online Machine Learning MCQss Questions with Answers

1. K-means is an unsupervised partitioning algorithm used to organize ————— data into clusters.

 Unlabeled

 Subcategorized

 Hierarchical

 Presorted

2. A random forest is an ensemble of decision-tree ————— that are trained on bootstrapped data.

 Variables

 Statements

 Observations

 Base learners

3. What process uses different “folds” (portions) of the data to train and evaluate a model across several iterations?

 Cross validation

 Proportional verification

 Model validation

 Grid search

4. What are some benefits of decision trees?

 When preparing data to train a decision tree, very little preprocessing is required

 Decision trees require no assumptions regarding the distribution of underlying data

 Decision trees enable data professionals to make predictions about future events based on currently available information

 When working with decision trees, overfitting is unlikely

5. In tree-based learning, how is a split determined?

 By the level of balance present among the predictions made by the model

 By the amount of leaves present

 By which variables and cut-off values offer the most predictive power

 By the number of decisions required before arriving at a final prediction

6. Naive Bayes is a supervised classification technique that is based on Bayes’ Theorem, with an assumption of ————- among predictors.

 Independence

 Clear hierarchy

 Even distribution

 Interdependence

7. When might you use a separate validation dataset?

 If you have very little data

 If you want to compare different model scores to choose a champion before predicting on test holdout data

 If you have a very large amount of data

 If you want to choose the specific samples used to validate the model

8. In a random forest, what type of data is used to train the ensemble of decision-tree-based learners?

 Sampled

 Duplicated

 Bootstrapped

 Unstructured

9. A data analytics team uses tree-based learning for a research and development project. Currently, they are interested in the parts of the decision tree that represent an item’s target value. What are they examining?

 Splits

 Roots

 Leaves

 Branches

10. When using a gradient boosting machine (GBM) modeling technique, which term describes a model’s ability to predict new values that fall outside of the range of values in the training data?

 Extrapolation

 Cross validation

 Learning rate

 Grid search

11. What is the only section of a decision tree that contains no predecessors?

 Split based on what will provide the most predictive power

 Decision node

 Leaf node

 Root node

12. What are some disadvantages of decision trees?

 Decision trees require assumptions regarding the distribution of underlying data

 Decision trees can be particularly susceptible to overfitting

 Preparing data to train a decision is a complex process involving significant preprocessing

 When new data is introduced, decision trees can be less effective at prediction

13. The supervised learning technique boosting builds an ensemble of weak learners ————-, then aggregates their predictions.

 Repeatedly

 In parallel

 Sequentially

 Randomly

14. Which section of a decision tree is where the final prediction is made?

 Root node

 Split

 Leaf node

 Decision node

15. In K-means, what term describes the point at which each cluster is defined?

 Coordinate

 Commonality

 Centroid

 Core

16. What are some of the benefits of ensemble learning?

 The predictions have less bias than other standalone models

 The predictions have lower variance than other standalone models

 It requires few base learners trained on the same dataset

 It combines the results of many models to help make more reliable predictions

17. In a decision tree, which node is the location where the first decision is made?

 Leaf

 Branch

 Root

 Decision

18. What is the only section of a decision tree that contains no predecessors?

 Leaf node

 Root node

 Decision node

 Split based on what will provide the most predictive power

19. Similar to a flow chart, a ——————- is a classification model that represents various solutions available to solve a given problem based on the possible outcomes of each solution.

 Linear regression

 Binary logistic regression

 Poisson distribution

 Decision tree

20. Which of the following statements correctly describes ensemble learning?

 Predictions using an ensemble of models can be accurate even when the individual models are barely more accurate than a random guess

 Ensemble learning involves aggregating the outputs of multiple models to make a final prediction

 If a base learner’s prediction is only slightly better than a random guess, it is called a “weak learner.”

 When building an ensemble using different types of models, each should be trained on completely different data

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Online Machine Learning MCQs Questions with Answers

• Naive Bayes is a supervised classification technique that is based on Bayes’ Theorem, with an assumption of ————- among predictors.
• K-means is an unsupervised partitioning algorithm used to organize ————— data into clusters.
• In K-means, what term describes the point at which each cluster is defined?
• Similar to a flow chart, a ——————- is a classification model that represents various solutions available to solve a given problem based on the possible outcomes of each solution.
• In tree-based learning, how is a split determined?
• In a decision tree, which node is the location where the first decision is made?
• In a random forest, what type of data is used to train the ensemble of decision-tree-based learners?
• What are some of the benefits of ensemble learning?
• When using a gradient boosting machine (GBM) modeling technique, which term describes a model’s ability to predict new values that fall outside of the range of values in the training data?
• The supervised learning technique boosting builds an ensemble of weak learners ————-, then aggregates their predictions.
• A data analytics team uses tree-based learning for a research and development project. Currently, they are interested in the parts of the decision tree that represent an item’s target value. What are they examining?
• What are some disadvantages of decision trees?
• Which section of a decision tree is where the final prediction is made?
• What is the only section of a decision tree that contains no predecessors?
• What are some benefits of decision trees?
• What is the only section of a decision tree that contains no predecessors?
• When might you use a separate validation dataset?
• What process uses different “folds” (portions) of the data to train and evaluate a model across several iterations?
• Which of the following statements correctly describes ensemble learning?
• A random forest is an ensemble of decision-tree ————— that are trained on bootstrapped data.

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