Which of the following is correct about the Naive Bayes Mcq?

What is the naïve assumption in a Naïve Bayes Classifier….Q.Which of the following is true about Naive Bayes ?B.b. assumes that all the features in a dataset are independentC.c. both a and bD.d. none of the above optionAnswer» c. c. both a and b

What does a naive Bayes algorithm assume Mcq?

The fundamental Naive Bayes assumption is that each feature makes an: independent. equal.

What is the main advantage of a Naive Bayes classifier compared to a decision tree?

Naive bayes does quite well when the training data doesn’t contain all possibilities so it can be very good with low amounts of data. Decision trees work better with lots of data compared to Naive Bayes. Naive Bayes is used a lot in robotics and computer vision, and does quite well with those tasks.

In which cases Naive Bayes is useful in classification Why?

The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.

What is the benefit of Naive Bayes in machine learning?

Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.

Why is Naive Bayes called naive Mcq?

Naive Bayes (NB) is ‘naive’ because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. … If this number is bigger then the corresponding calculation for class B then we say the measurement belongs in class A.

What are the advantages and disadvantages of decision trees?

Advantages and Disadvantages of Decision Trees in Machine Learning. Decision Tree is used to solve both classification and regression problems. But the main drawback of Decision Tree is that it generally leads to overfitting of the data.

What is naive in Naive Bayes?

Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.

What is the use of naive Bayes algorithm?

Naive Bayes uses a similar method to predict the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

Why is Naive Bayes naive in machine learning?

Why is it called Naïve Bayes? The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, Which can be described as: Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features.

Why is Naive Bayes called naive ques10?

A naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. Basically, it’s “naive” because it makes assumptions that may or may not turn out to be correct.

What are the pros and cons of using Naive Bayes?

Pros and Cons of Naive Bayes Algorithm
  • The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy.
  • It works well with high-dimensional data such as text classification, email spam detection.

Which is the disadvantage of Naive Bayes classifier?

Disadvantages of Naive Bayes

If your test data set has a categorical variable of a category that wasn’t present in the training data set, the Naive Bayes model will assign it zero probability and won’t be able to make any predictions in this regard.

Is naive Bayes supervised or unsupervised?

Naive Bayes classification is a form of supervised learning. It is considered to be supervised since naive Bayes classifiers are trained using labeled data, ie. … This contrasts with unsupervised learning, where there is no pre-labeled data available.

Is Naive Bayes accurate?

Naive Bayes classifier is the fast, accurate and reliable algorithm. Naive Bayes classifiers have high accuracy and speed on large datasets. Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features.