What is the decision boundary of this classifier?

A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut.

Can the decision boundary produced by naive Bayes algorithm be non linear?

What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a linear decision boundary) using the log odds demonstration. As we can see, the decision boundary is non-linear.

Does Naive Bayes generate decision rules?

While naive Bayes often fails to produce a good estimate for the correct class probabilities, this may not be a requirement for many applications. For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is more probable than any other class.

What are the key assumption for Naive Bayes?

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 decision boundary line?

A decision boundary is a line (in the case of two features), where all (or most) samples of one class are on one side of that line, and all samples of the other class are on the opposite side of the line. The line separates one class from the other.

What is decision boundary example?

The decision boundary For example, in the following graph, z=6−x1 represents a decision boundary for which any values of x1>6 will return a negative value for z and any values of x1<6 will return a positive value for z.

How do you find the Bayes decision boundary?

The formula for the Bayes decision boundary is given by equating likelihoods. We get an equation in the unknown z∈R2, giving a curve in the plane: ∑iexp(−5||pi−z||2/2)=∑jexp(−5||qj−z||2/2).

What is the decdecision boundary for a naive Bayes classifier?

Decision boundary for a Naive Bayes classifier is a piecewise quadratic function. More explanation is given at Formulate the naive Bayes weights as logistic regression instance, and then you have a decision boundary. Page on princeton.edu Make smart AI workforce decisions.

What is the naive Bayes assumption?

The Naive Bayes assumption implies that the words in an email are conditionally independent, given that you know that an email is spam or not. Clearly this is not true. Neither the words of spam or not-spam emails are drawn independently at random.

What is a Bayes classifier and how does it work?

Although this sounds like a pretty obvious solution, the reason it has the fancy name “Bayes classifier” is because of the decision theory underlying the method: the Bayes classifier minimizes loss when risk is defined as the probability of misclassification.

What is Bayes’ theorem?

The key idea of Bayes’ theorem is reversing the statistic using the overall rates. It says that the fraction of rich people who are happy is the fraction of happy people who are rich, times the overall fraction who are happy, divided by the overall fraction who are rich. So a pretty strong majority of rich people are happy.