kNN + Naive Bay

What does KNN stand for in machine learning?

• K-Nearest Neighbors

• Known Nearest Nodes

• Knowledge Network Navigator

• Kernel Nearest Network

In KNN, what does the 'K' represent?

• The number of clusters

• The distance metric

• The number of nearest neighbors to consider when making a prediction

• The number of features in the dataset

What kind of distance metrics are commonly used in KNN to find the nearest neighbors?

• Euclidean distance, Manhattan distance, or Minkowski distance

• Correlation distance, Angular distance, or Time-series distance

• Mahalanobis distance, Dice distance, or Edit distance

• Logistic distance, Fisher distance, or Dynamic distance

Is KNN more suitable for classification, regression, or both?

• Classification only

• Regression only

• Both, but it is most commonly used for classification

• Neither classification nor regression

What is a potential drawback of KNN regarding large datasets?

• KNN becomes more accurate with large datasets

• KNN loses its memory with large datasets

• KNN is unable to handle large datasets due to overfitting

• KNN can be computationally expensive and slow for large datasets

What is the key assumption made by Naive Bayes about features?

• All features are dependent on each other

• It assumes all features are independent of each other

• It assumes all features have the same distribution

• It assumes features are randomly distributed

Is Naive Bayes a probabilistic or deterministic algorithm?

• Probabilistic algorithm

• Deterministic algorithm

• Cluster-based algorithm

• Distance-based algorithm

In Naive Bayes, what theorem is used to calculate the probability of a hypothesis?

• • Pythagorean Theorem

• Bayes' Theorem

• Central Limit Theorem

• Euclidean Theorem

Which of the following best describes the role of prior probabilities in Naive Bayes?

• •  They are calculated from the training data and represent the overall probability of a class.

• • They are always set to be equal for all classes.

• •  They represent the likelihood of the features given the class.

• • They are only used when no training data is available.

How does Naive Bayes perform with high-dimensional data, and why?

• •  Poorly, because it requires significant computation power for large datasets.

• Well, because it is not affected by the curse of dimensionality.

• Poorly, because it struggles with highly correlated features.

• Well, because it ignores interactions between features.