Machine Learning with R Cognitive Class Exam Answers:- - Machine Learning Tutorials, Courses and Certifications

Machine Learning January 24, 2022 cognitive class Comments Off on Machine Learning with R Cognitive Class Exam Answers:- 5,788 Views

Question 1. Machine Learning was developed shortly (within the same century) as statistical modelling, therefore adopting many of its practices.

Question 2. Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

Question 3. Machine Learning is applied in current technologies, such as:

Question 1. In K-Nearest Neighbors, which of the following is true:

Question 2. A difficulty that arises from trying to classify out-of-sample data is that the actual classification may not be known, therefore making it hard to produce an accurate result.

Question 3. When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.

Question 1. Which of the following is generally true about the evaluation models: Train and Test on the Same Dataset and Train/Test Split.

Question 2. Which of the following is true about bias and variance?

Question 3. Root Mean Squared Error is the most popular evaluation metric out of the three discussed, because it produces the same units as the response vector, making it easy to relate information.

Question 1. What are some disadvantages that K-means clustering presents?

Question 2. Decision Trees tend to have high bias and low variance, which Random Forests fix.

Question 3. A Dendrogram can only be read for Agglomerative Hierarchical Clustering, not Divisive Hierarchical Clustering.

Question 1. Filters produce a feature set that does not contain assumptions based on the predictive model, making it a useful tool to expose relationships between features.

Question 2. Principle Components Analysis retains all information during the projection process of higher order features to lower orders.

Question 3. Which of the following is not a challenge to a recommendation system that uses collaborative filtering?

Question 1. Randomness is important in Random Forests because it allows us to have distinct, different trees that are based off of different data.

Question 2. When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.

Question 3. Which of the following is true?

Question 4. In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

Question 5. Which is the definition of entropy?

Question 6. Which of the following is true about hierarchical linkages?

Question 7. In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

Question 8. Which is true about bootstrapping?

Question 9. Machine Learning is still in early development and does not have much of an impact on the current society.

Question 10. In comparison to supervised learning, unsupervised learning has:

Question 11. Outliers are points that are classified by Density-Based Clustering that do not belong to any cluster.

Question 12. Which of the following is false about Linear Regression?

Question 13. Machine Learning uses algorithms that can learn from data without relying on standard programming practices.

Question 14. Which of the following are types of supervised learning?

Question 15. A Bottom Up version of hierarchical clustering is known as Divisive clustering. It is a more popular method than the agglomerative method.

Question 16. Which is NOT a specific outcome of how Dimensionality Performance improves production?

Question 17. Feature Selection is the process of selecting the variables that will be projected from a high-order dimension to a lower one.

Question 18. Hierarchical Clustering is one of the three main algorithms for clustering along with K-Means and Density Based Clustering.

Question 19. Which one is NOT a feature of Dimensionality Reduction?

Question 20. Low bias tends to create overly generalized models, which can cause a loss of relevant relations between the features and target output. When a model has low bias, we say that it “under fits” the data.