In simple words, the Naive Bayes algorithm works as the simple “probabilistic classifier”, which predicts the event/variable based on the probability of the independent data. In more detail, you can read the explanation here. Below we’ll discuss the practical implementation of this algorithm for business planning and decision making.

**What are the strengths and weaknesses of Naive Bayes?**

*Plus:*

- The algorithm is recommended to use for the prediction of the class of dataset. Additionally, Naive Bayes works well with multiclass predictions. Such model can be used not only to predict positive and negative publications but to recognize the neutral ones.
- On the strong side of the algorithm, we can mention that it demands smaller datasets for the teaching of the model compared to regression model training.
- Naive Bayes performs incredibly well with categorical input variables. To demonstrate correct classification for numerical variables, the gaussian=normal distribution of the variables is a demand.

*Minus:*

- One of the demands for algorithm operability, we have to provide independent variables. This means that all the features have an equal effect on the dependent variable.
- To the weak side of the classification algorithm, we can put the fact that if the category was not represented in the test data set, then the classification algorithm will be unable to make a prediction. This situation is often called zero frequency.
- The next limitation is the demand of providing independent predictors. This means that if we give the data, which already depend on each other, the classification will be malformed.

Naive Bayes is the most straightforward and fastest algorithm for categorization. It is suitable for a large group of data. The Naive Bayes classifier has succeeded in various applications such as spam filtering, text classification, sentiment analysis, and recommended systems. It uses Bayes’s Theorem of Probability for the prediction of the unknown category.

**Applications of Naive Bayes Classification**

**Predict cancer disease**

We are moving to real cases from some theoretical facts, when Naive Bayes is solving the business problem or even saving human life.

One such example is the implication of the machine learning algorithm for the prediction of cancer. We know that the test results taken from the same patient can vary a lot based on external factors. This biological uncertainty makes it harder to predict particular illnesses and diseases.

By introducing to the machine learning model different datasets containing the biological data, which represent the cancer disease, we can teach it. Finally, with the Naive Bayes classifier, we can predict if the patient is ill or healthy. Read more about this case on biomedcentral.com.

Predicting patient outcomes from genome-scale measurements holds significant promise for improving clinical care. A large number of measurements, however, makes this task computationally challenging. Introduction of computer modeling for laboratory test analysis to predict patient outcomes based on genome-scale data is an excellent step towards early disease diagnostics and a healthy nation.

**The text classification with Multinomial Naive Bayes classifier algorithm.**

Text classification is solving the problem of automatically assigning provided text example to predefined categories. Due to the rapid explosion of texts in digital form, such classification became an important research area, owing to the need to automatically organize and index extensive text collections. Text classification is a multi-dimensional problem. Naive Bayes works perfectly well with the determination of spammed/non spammed texts and can conduct the classification of different literary genres for the provided articles. In document classification, two variants of Naive Bayes are commonly employed. The multivariate Bernoulli model uses Naive Bayes with each word in a corpus represented by a binary variable that is true if and only if the word is present in a document. However, only the words that are present in the paper are to be considered when calculating the probabilities for that document.

**How the categorization works**

The classification has two phases, a learning phase and the evaluation phase. In the learning phase, the classifier trains its model on a given dataset, and in the evaluation phase, it tests the classifier performance. Performance is estimated based on different parameters such as accuracy, error, precision, and recall. The half of success for the precise classification results lay in the data preparation. So, by providing the data, cleared from the mess, duplicates, anomalies (extremum values can be recognized as anomalies for numerical variables, btw), we can guarantee accurate results for the Naive Bayes algorithm model analysis.

Think about the article as the set of nouns, adjectives, prepositions, and verbs connected with each other in the predictable order. Imagine the twines of nouns and verbs, which are often used together. Multiply this example to the vast data sets available for the machine analysis on different platforms, on youtube, and in electronic libraries. Based on this data, the ML model can easily be taught to recognize patterns, which are often used in particular documents, such as emails. Such patterns can be labeled as spam. For each set, we follow the same steps to extract word features. All characters were converted to lowercase, only alphabetic tokens were considered, stopwords were removed, and the full vocabulary was used.

Another way for text analysis: recognized rare combinations of words and weird syntax can signal auto-generated content.

One more example of multinomial Bayes algorithm implementation can be the structuring of the articles by genres. Such classification is useful for the publication of the films or books or providing the website visitors’ recommendation to pay attention to the publications of a similar topic.

It’s worth mentioning that a lot of effort must be put into the preliminary stage: data preparation. If we are working with the task of the article categorization work, we have the multivariate solution.

Some articles can be recognized as examples of multiple topics. In this case, we need to create duplicate items with each piece corresponding to one topic for the correct model training. This inevitably reduces the accuracy of our output but simplifies the task.

Working with multinomial algorithms, we always have to compromise between too complicated classification procedures and the accuracy level.

**Conclusion**

The first introduction of the model was described by H. Harris in 1966 and formalized in 1972. Nowadays, it is one of the most important algorithms and has been adopted as a standard feature in many NLP frameworks.