The Naive Bayes classifier is one of a series of extremely simple Bayes Theorem-based probabilistic classifiers. The classifier was given the moniker “Naive Bayes” – it’s also known as “Idiot Bayes” in certain books – since the computations for each class were reduced to make them tractable. The Naive Bayes model is essentially a conditional probability classification using the Bayes Theorem.
The chance of an event occurring is known as conditional probability. Another cool thing we can do using conditional probability is compute the joint probability, which is the likelihood of two or more occurrences occurring at the same time. On the other hand, the joint probability might be used to determine the conditional probability; however, because the joint probability is sometimes difficult to calculate, we apply Bayes Theorem to calculate the conditional probability.
In ML there are three primary categories of learning problems:
- Learning that is supervised
- Learning Without Supervision
- Learning through Reinforcement
The Naive Bayes algorithm is commonly used for supervised learning tasks. Supervised learning can be divided into regression tasks, in which the model learns to predict continuous values, and classification tasks, in which the model learns to predict a category/class – Naive Bayes belongs to the latter category.
Utilization
Despite the Naive Bayes classifier’s assumption that each input is independent of all other variables, which is a strong assumption given that variables seldom interact in real-world data, the Naive Bayes classifier performs admirably on a variety of tasks. Consider the following example:
Document classification entails assigning a document to one or more classes or categories, for categorizing a news story into categories such as sport, business, politics, and so on. This includes spam categorization as well.
Real-Time Prediction: Because the Naive Bayes algorithm is a quick eager learning classifier, it may be used to produce predictions in real-time.
Predicting instances into one of three or more classes is known as a multi-class prediction.
Sentiment analysis is one of the techniques used in Natural Language Processing. The goal is to figure out if the data is good or negative.
Because ML systems are more fragile than you think. All based on our open-source core.
Open SourceAdvantages and disadvantages of Naive Bayes
- Naive Bayes is a straightforward model that is simple to implement. It also provides rapid predictions and can handle multi-class prediction jobs
- It has a variety of capabilities that work well with data, such as text categorization and email spam detection.
- It doesn’t take a lot of training data to discover intriguing insights, and when the data set is tiny, it may outperform complicated machine learning models.
- In the actual world, the Naive Bayes classifier’s premise of all variables being independent frequently holds false.
Improve Naive Bayes model
- If continuous features do not have a normal distribution, transformation or other procedures should be used to convert them to one.
- If the test data set has a zero frequency problem, use Laplace correction smoothing techniques to estimate the test data set’s class.
- Remove linked features since they are voted twice in the model, which might lead to an overestimation of relevance.
- Naive Bayes classifiers include a limited number of parameter tuning choices, such as alpha=1 for smoothing, fit prior=[True|False] to learn or not learn class prior probabilities, and a few more.
- You may consider using a classifier combination approach like ensembling, bagging, or boosting, however, these techniques will not assist.
Recap
Despite using too simplistic data assumptions, the Naive Bayes classifier has shown to be very successful in a variety of real-world applications. Machine learning has had a significant influence on all industries that have adopted it as part of their workflow. Many more firms across a wide range of sectors are beginning to invest in AI and ML since it is thought that these technologies will be critical to long-term corporate success.
