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Naive Bayes Classifier: Pros & Cons, Applications & Types Explained

When you need a fast problem-solving algorithm, where do you go? You go to the Naive Bayes classifier. It’s a quick and simple algorithm that can solve various classification problems. In this article, we’ll understand what this algorithm is, how it works, and what its qualities are. Let’s get started. 

What is the Naive Bayes Classifier?

The Naive Bayes classifier separates data into different classes according to the Bayes’ Theorem, along with the assumption that all the predictors are independent of one another. It assumes that a particular feature in a class is not related to the presence of other features. 

For example, you can consider a fruit to be a watermelon if it is green, round and has a 10-inch diameter. These features could depend on each other for their existence, but each one of them independently contributes to the probability that the fruit under consideration is a watermelon. That’s why this classifier has the term ‘Naive’ in its name. 

This algorithm is quite popular because it can even outperform highly advanced classification techniques. Moreover, it’s quite simple, and you can build it quickly. 

Here’s the Bayes theorem, which is the basis for this algorithm:

P(c | x) = P(x | c) P(c)/P(x)

In this equation, ‘c’ stands for class, and ‘x’ stands for attributes. P(c/x) stands for the posterior probability of class according to the predictor. P(x) is the prior probability of the predictor, and P(c) is the prior probability of the class. P(x/c) shows the probability of the predictor according to the class. 

Read: Naive Bayes Explained

Advantages of Naive Bayes

  • This algorithm works very fast and can easily predict the class of a test dataset. 
  • You can use it to solve multi-class prediction problems as it’s quite useful with them. 
  •  Naive Bayes classifier performs better than other models with less training data if the assumption of independence of features holds. 
  • If you have categorical input variables, the Naive Bayes algorithm performs exceptionally well in comparison to numerical variables. 

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. This phenomenon is called ‘Zero Frequency,’ and you’ll have to use a smoothing technique to solve this problem.
  • This algorithm is also notorious as a lousy estimator. So, you shouldn’t take the probability outputs of ‘predict_proba’ too seriously. 
  • It assumes that all the features are independent. While it might sound great in theory, in real life, you’ll hardly find a set of independent features. 

Applications of Naive Bayes Algorithm

As you must’ve noticed, this algorithm offers plenty of advantages to its users. That’s why it has a lot of applications in various sectors too. Here are some applications of Naive Bayes algorithm:

  • As this algorithm is fast and efficient, you can use it to make real-time predictions.
  • This algorithm is popular for multi-class predictions. You can find the probability of multiple target classes easily by using this algorithm.
  • Email services (like Gmail) use this algorithm to figure out whether an email is a spam or not. This algorithm is excellent for spam filtering.
  • Its assumption of feature independence, and its effectiveness in solving multi-class problems, makes it perfect for performing Sentiment Analysis. Sentiment Analysis refers to the identification of positive or negative sentiments of a target group (customers, audience, etc.)
  • Collaborative Filtering and the Naive Bayes algorithm work together to build recommendation systems. These systems use data mining and machine learning to predict if the user would like a particular resource or not. 

Also Read: Machine Learning Models Explained

Types of Naive Bayes Classifier

This algorithm has multiple kinds. Here are the main ones:

Bernoulli Naive Bayes

Here, the predictors are boolean variables. So, the only values you have are ‘True’ and ‘False’ (you could also have ‘Yes’ or ‘No’). We use it when the data is according to multivariate Bernoulli distribution. 

Multinomial Naive Bayes

People use this algorithm to solve document classification problems. For example, if you want to determine whether a document belongs to the ‘Legal’ category or ‘Human Resources’ category, you’d use this algorithm to sort it out. It uses the frequency of the present words as features. 

FYI: Free Deep Learning Course!

Gaussian Naive Bayes

If the predictors aren’t discrete but have a continuous value, we assume that they are a sample from a gaussian distribution. 

Conclusion

We hope you found this article useful. If you have any questions related to the Naive Bayes algorithm, feel free to share them in the comment section. We’d love to hear from you.

If you’re interested to learn more about AI, machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

What are the limitations of Naive Bayes?

The naive Bayes classifier is an algorithm used to classify new data instances using a set of known training data. It is a good algorithm for classification; however, the number of features must be equal to the number of attributes in the data. It is computationally expensive when used to classify a large number of items. It is not suitable for numerical data. It can only work when the features are independent of each other. It is not suitable when the feature-values are nominal. It requires that the feature-values be mutually exclusive. It requires that the frequency of the feature-values be proportional to the probability that they are correct.

What is the biggest advantage and disadvantage of Naive Bayes classifiers?

The biggest advantage of Naive Bayes is that it can work with very small data sets. It is one of the most popular algorithms for spam filtering. Also, it is relatively simple to implement. It is almost always used as a classifier. If a data set is not available, one can still use it as a classification algorithm. This algorithm is used in e-mail spam filtering, it is also used by Google to classify web pages. However, it might not be as effective in more complex classification problems. It can only work when the features are independent of each other.

How do I stop Overfitting in Naive Bayes?

One reason for overfitting is having the wrong training data. If you have a training data set with a lot of noise and you have a lot of training examples, the classifier will look at the noise in the training data and not the underlying pattern that you are trying to build a model for. Another reason is that your model is just too complex. If you have a model where a small change in input can cause a large change in output you can get overfitting. Another solution is to use regularization. Regularization will shrink long branches in your model. It smooths out your model and prevents overfitting.

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