The correct answer is (A) Assumes that all the features in a dataset are independent and (B) Assumes that all the features in a dataset are equally important. Therefore, the most accurate answer would be (C) Both.

Naive Bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that a particular fruit is an apple or an orange or a banana and that is why it is known as ‘Naive’.

Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

So, the assumption that all features in a dataset are independent is a characteristic of Naive Bayes, which makes option (A) correct.

Also, in practice, the independence assumption is often violated, but Naive Bayes classifiers still tend to perform very well under this unrealistic assumption. Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful alternatives.

So, the assumption that all features in a dataset are equally important is also a characteristic of Naive Bayes, which makes option (B) correct.

Therefore, the correct answer is (C) Both.

Question

The correct answer is (A) Assumes that all the features in a dataset are independent and (B) Assumes that all the features in a dataset are equally important. Therefore, the most accurate answer would be (C) Both.

Naive Bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that a particular fruit is an apple or an orange or a banana and that is why it is known as ‘Naive’.

Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

So, the assumption that all features in a dataset are independent is a characteristic of Naive Bayes, which makes option (A) correct.

Also, in practice, the independence assumption is often violated, but Naive Bayes classifiers still tend to perform very well under this unrealistic assumption. Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful alternatives.

So, the assumption that all features in a dataset are equally important is also a characteristic of Naive Bayes, which makes option (B) correct.

Therefore, the correct answer is (C) Both.

Knowee AI · Accepted Answer

The correct answer is (A) Assumes that all the features in a dataset are independent and (B) Assumes that all the features in a dataset are equally important. Therefore, the most accurate answer would be (C) Both.

Naive Bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that a particular fruit is an apple or an orange or a banana and that is why it is known as ‘Naive’.

Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

So, the assumption that all features in a dataset are independent is a characteristic of Naive Bayes, which makes option (A) correct.

Also, in practice, the independence assumption is often violated, but Naive Bayes classifiers still tend to perform very well under this unrealistic assumption. Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful alternatives.

So, the assumption that all features in a dataset are equally important is also a characteristic of Naive Bayes, which makes option (B) correct.

Therefore, the correct answer is (C) Both.

Which of the following is correct about the Naive Bayes?*1 point(D) None of these(B) Assumes that all the features in a dataset are equally important(A) Assumes that all the features in a dataset are independent(C) Both

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