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

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

 By the amount of leaves present

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

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

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

 Decision node

 Leaf node

 Split

 Root node

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

 Centroid

 Core

 Commonality

 Coordinate

4. 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?

 Branches

 Leaves

 Splits

 Roots

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

 Split based on what will provide the most predictive power

 Leaf node

 Decision node

 Root node

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

 Presorted

 Subcategorized

 Unlabeled

 Hierarchical

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

 Root

 Decision

 Leaf

 Branch

8. What are some benefits of decision trees?

 When working with decision trees, overfitting is unlikely

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

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

 Even distribution

 Interdependence

 Independence

 Clear hierarchy

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

 Duplicated

 Sampled

 Unstructured

 Bootstrapped

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

 Randomly

 In parallel

 Repeatedly

 Sequentially

12. 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?

 Cross validation

 Learning rate

 Extrapolation

 Grid search

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

 Observations

 Variables

 Base learners

 Statements

14. 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.

 Poisson distribution

 Binary logistic regression

 Linear regression

 Decision tree

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

 Model validation

 Grid search

 Proportional verification

 Cross validation

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

 Leaf node

 Decision node

 Split based on what will provide the most predictive power

 Root node

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

 It requires few base learners trained on the same dataset

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

 The predictions have less bias than other standalone models

 The predictions have lower variance than other standalone models

18. What are some disadvantages of decision trees?

 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

 Decision trees can be particularly susceptible to overfitting

 Decision trees require assumptions regarding the distribution of underlying data

19. When might you use a separate validation dataset?

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

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

 If you have a very large amount of data

 If you have very little data

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

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

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

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

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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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