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Naive Bayes Advantages

Naive Bayes is a really unique machine learning algorithm because it doesn’t learn through gradient descent mechanism. Instead Naive Bayes calculates its own parameters in a very fast calculation.

This causes Naive Bayes to be an astonishingly fast machine learning algorithm compared to most of its ML algorithm competition which learn through iterative gradient descent process or distance calculations.

Naive Bayes is commonly used and preferred in suitable classification tasks. In this article we will list and elaborate some of its most obvious advantages in more detail.

1- Very Fast

Naive Bayes machine learning models have very fast training and prediction phases.

Because of its high performance Naive Bayes can be preferred in real time machine learning deployment or as a complimentary model to improve less speedy models in a hybrid solution.

You can see more details about Naive Bayes runtime performance and computation complexity in this article:

Naive Bayes Performance

2- Simple

Naive Bayes implementation is also simple and straightforward. All it takes is a formula of Naive Bayes Theorem to have a functioning machine learning model with Naive Bayes.

P(A|B) = ( P(B|A) * P(A) ) / P(B)

You can read more about the explanation of Naive Bayes Theorem and its probabilities in the following article:

Tuning Naive Bayes


3- Powerful

In addition to being very fast, Naive Bayes can be one of the best classifier in suitable applications. i.e.: Data Mining and Text Classification are known to be its strong suits.

It’s known to show amazing performance in data mining and text classification.

This is mind blowing especially considering it’s almost a 300 year old theorem.

For more details, see:

Naive Bayes History

Stable and Robust

4- Probabilistic Classifier

Naive Bayes is also a probabilistic classifier. This means it will produce probability reports which can be very useful if you need them.

Probability reports don’t just tell target label of a sample but they also tell the probability of the prediction. If a machine learning project is tied to these probabilities you can have an increased control on the classification process by allowing or disallowing predictions below a certain probability percentage.

Probability reports are also very useful to create hybrid machine learning solutions where values with certain probability trigger the process of another machine learning model.

Naive Bayes Variants

5- Multiclass Prediction Capability

Different Naive Bayes variants exist for different types of data. While Gaussian Naive Bayes can handle continuous data, Bernoulli Naive Bayes works great with binary and Multinomial Naive Bayes can be used to classify categorical discrete multiclass datasets.

This offers great versatility and enables many advantages for text related classification implementations. There is even Complement Naive Bayes which is a variant of Multinomial Naive Bayes and it aims to improve model accuracy when working with imbalanced data by reducing bias through a complement approach.

Stable and Robust

6- Probabilistic Classifier

Another advantage of Naive Bayes is its high performance even with little training data. Some machine learning models will require lots of data for training and perform poorly if it’s not provided. Naive Bayes usually performs very well with moderate sizes of training datasets.

One problem that can occur when a sample is not previously introduced to Naive Bayes model and without smoothing model thinks its probability should be 1 or 0, both inaccurate and illogical assumptions. 

See: Naive Bayes Disadvantages

Thanks to Laplace, and his smoothing improvement to Bayes Theorem Naive Bayes can be optimized to perform in more sensible ways in such situations.

See: Tuning Naive Bayes

Stable and Robust

7- Data Mining and Text Classification

Naive Bayes thrives in data mining and text classification tasks. Fast and accurate multiclass prediction capability means solutions that scale well and can be used to make real-time AI deployment.

Email services are known to use this excellent classification algorithm since mid-90s to detect and filter out spam emails. It makes perfect sense due to:

  • Scalability
  • Great runtime performance
  • Data mining, text classification and semantics success
  • Multiclass prediction capabilities

Naive Bayes is a potent classifier


Overall Naive Bayes has much to offer and it remains a popular classifier in 2021. It has some unique advantages such as blazing fast speeds and probabilistic output.

It’s a shame this simple, fun and very useful model can only make classification predictions. For continuous value predictions you can refer to another useful machine learning algorithm such as: