What are the Pros and Cons of using Naive Bayes? Write any four Applications of Naive Bayes Algorithm.

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. 

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Applications

Below are a few use cases that employ Naive Bayes:

• Real-time prediction: Naive Bayes is an eager learning classifier and is quite fast in its execution. Thus, it could be used for making predictions in real-time.

• Multi-class prediction: The Naive Bayes algorithm is also well-known for multi-class prediction, or classifying instances into one of several different classes.

• Text classification/spam filtering/sentiment analysis: When used to classify text, a Naive Bayes classifier often achieves a higher success rate than other algorithms due to its ability to perform well on multi-class problems while assuming independence. As a result, it is widely used in spam filtering (identifying spam email) and sentiment analysis (e.g. in social media, to identify positive and negative customer sentiments).

• Recommendation Systems: A Naive Bayes Classifier can be used together with Collaborative Filtering to build a Recommendation System that could filter through new information and predict whether a user would like a given resource or not.

Advantages of Naive Bayes

• Naive Bayes is easy to grasp and works quickly to predict class labels. It also performs well on multi-class prediction.

• When the assumption of independence holds, a Naive Bayes classifier performs better compared to other models like logistic regression, and you would also need less training data.

• It performs well when the input values are categorical rather than numeric. In the case of numerics, a normal distribution is assumed to compute the probabilities (a bell curve, which is a strong assumption).

Disadvantages of Naive Bayes

• If a categorical variable has a category in the test data set, which is not observed by the model in the training data, it will assign a 0 (zero) probability and not be able to make a prediction. This is often referred to as “Zero Frequency”. To solve this problem, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.
• The other limitation of Naive Bayes is the assumption of independence among the features. In real life, it is almost impossible that we get a set of features that are completely independent.

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