Table of Contents
- 1 Why is naive Bayes a decent classifier but a bad estimator?
- 2 Which type of naive Bayes classifier is usually used for Yes No type Boolean predictors?
- 3 What is the solution to the issue of naïve Bayes classifier that assumes features are independent?
- 4 How does a naive Bayes classifier use Bayes rule?
- 5 What is the naïve assumption in a naïve Bayes classifier?
- 6 What is the correlate assumption for a naïve Bayes classifier?
Why is naive Bayes a decent classifier but a bad estimator?
One of the simplest smoothing techniques is called Laplace estimation . On the other side naive Bayes is also known as a bad estimator, so the probability outputs are not to be taken too seriously. Another limitation of Naive Bayes is the assumption of independent predictors.
Which type of naive Bayes classifier is usually used for Yes No type Boolean predictors?
Bernoulli Naive Bayes: This is similar to the multinomial naive bayes but the predictors are boolean variables. The parameters that we use to predict the class variable take up only values yes or no, for example if a word occurs in the text or not.
What is the solution to the issue of naïve Bayes classifier that assumes features are independent?
To overcome this issue, naive bayes algorithm assumes that all features are independent of each other. Furthermore, denominator (p(x1,x2, … , xn)) can be removed to simplify the equation because it only normalizes the value of conditional probability of a class given an observation ( p(yi | x1,x2, … , xn)).
Can I use naive Bayes for classification?
Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems.
How do I use Naive Bayes?
Naive Bayes Tutorial (in 5 easy steps)
- Step 1: Separate By Class.
- Step 2: Summarize Dataset.
- Step 3: Summarize Data By Class.
- Step 4: Gaussian Probability Density Function.
- Step 5: Class Probabilities.
How does a naive Bayes classifier use Bayes rule?
What is Naive Bayes algorithm? It 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.
What is the naïve assumption in a naïve Bayes classifier?
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. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter.
What is the correlate assumption for a naïve Bayes classifier?
Naive Bayes Classifier belongs to the family of probabilistic classifiers and is based on Bayes’ theorem. It is based on the assumption that the presence of one feature in a class is independent to the other feature present in the same class.