Naive Bayes Classifier: Everything You Need to Know

Introduction

If you are interested in machine learning, artificial intelligence, or data science, you must know the importance of assumptions. Machine learning or other risk-based models are developed based on certain assumptions and predefined conventions that allow developers to get the desired results. If the developers do not consider the assumptions while building models, it can interfere with data and lead to inaccurate results. The Naive Bayes Classifier is one of the classic examples of mathematical assumptions in statistical models. 

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This blog explains the Bayes theorem, the Naive Bayes Classifier, and its different models. 

What is the Naive Bayes Classifier?

The Naive Bayes Classifier is based on the Bayes theorem propounded by Thomas Bayes, a British mathematician. So before you understand the Naive Bayes Classifier, it is pertinent to know the Bayes theorem. The Bayes theorem, also known as Bayes’ Law or Bayes’ Rule, determines the chances of occurrence or non-occurrence of any event. In simple terms, it tells the probability of an event taking place.

Bayes theorem is popularly used in machine learning to predict classes accurately. It calculates the conditional probability of classification tasks in machine learning. The classification tasks refer to the activities performed by machine learning algorithms to solve problems. You can understand this better with the example of spam emails. The machine learning algorithm learns to classify emails as spam or not spam. Therefore, in a machine learning model, the Bayes theorem is used to predict the classification or segregation activities.

The Naive Bayes theorem is a subset of the Bayes theorem. Since its primary function is the classification of tasks, we also refer to it as the Naive Bayes Classifier. The theorem also makes a naive assumption that all class features are not dependent on each other, hence the term Naive Bayes theorem. If we discuss machine learning, the Naive Bayes Classifier is an algorithm that applies the Bayes theorem to predict an event while assuming that the attributes of a particular class are independent of each other. These attributes are also considered equal and can exist without depending on another feature. 

We can use the Naive Bayes Classifier for many functions, such as diagnosing a specific disease if a set of symptoms is available, weather forecast, humidity, temperature, and other factors. In simple words, you can use the naive Bayes algorithm for any data process which requires binary or multi-use multiclass classification. Naive Bayes Classifier works on the concept of conditional probability. It means that the probability of one event taking place depends on the occurrence of any other event. For example, the conditional probability of event A happening is dependent on the occurrence of event B.

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Working of Naive Bayes Classifier

The Naive Bayes Classifier is used to find output probability if the input is available. Naive Bayes Classifier solves prediction modeling problems by categorizing classes using different labels. Machine learning algorithms based on a probability model can solve such complex challenges.

For example, there are y1, y2, y3…….yn class labels and x1, x2, x3……xk input variables in a classification problem. First, we need to calculate the conditional probability of a different y class label in the case of x inputs. Then we consider the feature with the highest conditional probability as the most suitable classification.

Different models of Naive Bayes Classifier 

These are three types of Naive Bayes classifiers.

• Gaussian Naive Bayes – The Gaussian Naive Bayes uses normal or Gaussian distribution to support continuous data. The normal distribution theorem analyzes data if there is a probability that the continuous data will be equally distributed above or below the mean.
• Multinomial Naive Bayes – We use the multinomial Naive Bayes classifier when the classification of discrete features is required, for example, word counts for text classification. It statistically analyzes the content of a document and assigns it to a class.
• Bernoulli Naive Bayes – The Bernoulli Naive Bayes is similar to the Multinomial Naive Bayes. It is also used for discrete data. However, it accepts only binary features – 0 and 1. So, in the case of binary features in the dataset, we have to use Bernoulli Naive Bayes.

Advantages and Disadvantages of the Naive Bayes Classifier 

The most significant feature of the Naive Bayes Classifier is it can manage both continuous and discrete data. The accuracy of the Naive Bayes Classifier increases with the amount of data as it gives more accurate results if a large dataset is used. Here are some advantages and disadvantages of the Naive Bayes Classifier.

Advantages of Naive Bayes Classifier

• Highly scalable – One of the most significant advantages of the Naive Bayes Classifier is it is highly scalable because of its naive assumption.
• Less training period – We need a small amount of training data to train the Naive Bayes Classifier. So, the training period is relatively short compared to other algorithms.
• Simple – Another significant advantage of the Naive Bayes Classifier is that it is simple to build. Also, it can be easily used to classify large datasets.

Disadvantages of Naive Bayes Classifier

• Limitations in real-world uses – The Naive Bayes Classifier makes a naive assumption that the various features of a class are independent of each other. Since this phenomenon rarely happens in the real world, the algorithm can be used for limited purposes. 
• Zero-frequency problem – If the training data set had a missing value-added later, the Naive Bayes Classifier marks that value as zero because of no frequency. So, when the probabilities of different values are multiplied, the frequency-based probability comes to zero because the algorithm has assigned zero values to the missing data, which can lead to inaccurate results.

Use of Naive Bayes Classifier in Machine Learning and Artificial Intelligence

The Naive Bayes algorithm is beneficial in machine learning and artificial intelligence because of its assumption that all the attributes of a class are not dependent on each other. Here are some practical uses of the Naive Bayes Classifier in Machine Learning and Artificial Intelligence:

• Predicting colon cancer –  Researchers have suggested using a Naive Bayes Classifier model to predict colon cancer. It can be one of the most remarkable uses of the Naive Bayes Classifier. It can be made possible with colon cancer data like hemoglobin range, and the red and white blood cells count in the body of colon patients as training data for the model. The algorithm can predict colon cancer if a patient’s hemoglobin and blood cells come within the same range.
• Traffic risk management – The Naive Bayes Classifier can also be used for traffic risk management. The Naive Bayes Classifier can predict the driver’s driving risk and road traffic based on training data.

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Conclusion

Naive Bayes Classifier is a beginner-friendly algorithm that simplifies classification in machine learning and artificial intelligence. The Naive Bayes algorithm is used for various practical applications like spam protection, weather forecasts, and medical diagnosis using ML and Ai. So, if you have a keen interest in machine learning and wish to pursue a career in this field, you must know about Naive Bayes Classifier and other basic algorithms. You can pursue a Master of Science in Machine Learning and Artificial Intelligence from upGrad to learn algorithms and other ML and AI skills in-depth. The course also provides an opportunity to work on real-life machine learning projects, allowing you to acquire skills, enhance your CV, and grab several job opportunities in AI and ML.

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