Which of the following statements about the K Nearest Neighbours and Gaussian Naive Bayes classifiers are true?Group of answer choicesThe complexity involved in running the KNN algorithm increases with more training samples because it will have to calculate more distances but the complexity of the Gaussian Naive Bayes algorithm does not.Both classifiers can be used on any given classification problem.When there are more than three features, we must use Gaussian Naive Bayes as KNN can only deal with three features given that Euclidean distance is only defined for up to three dimensions.The performance of both classifiers are expected to improve when the number of training samples increases
Question
Which of the following statements about the K Nearest Neighbours and Gaussian Naive Bayes classifiers are true?Group of answer choicesThe complexity involved in running the KNN algorithm increases with more training samples because it will have to calculate more distances but the complexity of the Gaussian Naive Bayes algorithm does not.Both classifiers can be used on any given classification problem.When there are more than three features, we must use Gaussian Naive Bayes as KNN can only deal with three features given that Euclidean distance is only defined for up to three dimensions.The performance of both classifiers are expected to improve when the number of training samples increases
Solution
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The complexity involved in running the KNN algorithm increases with more training samples because it will have to calculate more distances but the complexity of the Gaussian Naive Bayes algorithm does not. - TRUE. KNN is a lazy learner, meaning it does not learn a discriminative function from the training data but memorizes the training dataset instead. Therefore, the computational cost at test time increases with the size of the training dataset. On the other hand, Gaussian Naive Bayes classifier makes an assumption of independence among predictors, which simplifies the calculation and does not increase the complexity with more training samples.
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Both classifiers can be used on any given classification problem. - FALSE. The choice of classifier depends on the specific problem and the nature of the data. For example, KNN might not perform well on high-dimensional data due to the curse of dimensionality, while Naive Bayes assumes that all features are independent, which might not hold true for all datasets.
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When there are more than three features, we must use Gaussian Naive Bayes as KNN can only deal with three features given that Euclidean distance is only defined for up to three dimensions. - FALSE. Euclidean distance can be calculated in any number of dimensions, not just three. Therefore, KNN can be used with more than three features.
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The performance of both classifiers are expected to improve when the number of training samples increases. - TRUE. In general, the performance of machine learning algorithms improves with more training data, as they can learn more about the underlying patterns in the data. However, this is not always the case, as the quality of the data and the relevance of the features also play a crucial role.
Similar Questions
In K Nearest Neighbours classification, which of the following statements about K and N (the number of training samples) is/are true?Group of answer choicesK impacts both the accuracy and the computational complexity of the KNN algorithm.K and N should be linearly related, i.e. K/N should be a pre-determined constant.The time taken to perform the classification task increases with N.K should be odd to avoid ties.
Question 4(True/False) K Nearest Neighbors with large k tend to be the best classifiers.1 pointTrueFalse
Which one of the following statements is true regarding K Nearest Neighbors?1 pointFor high dimensional data, the best distance measure to use for KNN is the Euclidean distance.The Manhattan distance between two data points is the square root of the sum of the squares of the differences between the individual feature values of the data points.The distance between two data points is independent of the scale of their features.K Nearest Neighbors (KNN) assumes that points which are close together are similar.
Question 3Which one of the following statements is most accurate about K Nearest Neighbors (KNN)? 1 pointKNN is a classification model.KNN is an unsupervised learning method. KNN is a regression model.KNN can be used for both classification and regression.
When applying k-Nearest Neighbors (KNN) for classification, what is the role of the "k" parameter?a.It specifies the number of dimensions in the dataset.b.It determines the learning rate in the algorithm.c.It defines the number of clusters.d.It sets the number of nearest neighbors to consider for classification.
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