What are the advantages of the classifiers based on Bayes Theorem?

Advantages of Naive Bayes Classifier It is simple and easy to implement. It doesn’t require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points.

What is the significance of Bayes classifier?

The Bayes classifier is a useful benchmark in statistical classification. (possibly depending on some training data) is defined as. Thus this non-negative quantity is important for assessing the performance of different classification techniques.

What is the solution to the issue of the 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)).

How is classification done using Bayes classifier?

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. To start with, let us consider a dataset.

Why is naive Bayes classifier called naive?

Naive Bayes is a simple and powerful algorithm for predictive modeling. 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.

Is Bayes classifier the best classifier?

The bayes classifier is the theoretically optimal classifier for a given classification problem. This is why it is also called the target classifier: it is the classifier we aim at when using learning algorithms.

How naïve Bayes classifier is used in real life?

Applications. Real time Prediction: Naive Bayes is an eager learning classifier and it is sure fast. Thus, it could be used for making predictions in real time. Multi class Prediction: This algorithm is also well known for multi class prediction feature.

Why do we use naive Bayes classifier?

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 naive Bayes classifier called naive?

What is the benefit of Nave?

Testing

What is optimal Bayes classifier?

The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. Bayes Optimal Classifier is a probabilistic model that finds the most probable prediction using the training data and space of hypotheses to make a prediction for a new data instance.