Naive Bayesian Model
Several machine learning algorithms rank Naive Bayes among their top echelons.
Machine learning is a widely used and popular technique for dealing with classification problems.
Using the Naive Bayes algorithm in machine learning, we can make decisions based on Bayes's Theorem. Probability itself is a theory that is based on Bayes' theorem. Pixelette Technologies will thoroughly cover all these concepts. A Naive Bayes algorithm cannot function without Bayes' Theorem. The Naive Bayes method determines the probability of a record belonging to a class based on the values of features, thus giving us the conditional probability to classify the data. What we have here is nothing more than conditional probability! All the features of Naive Bayes must be independent of each other. This is probably the most important assumption of the approach. If the continuous variables need to be converted into discrete variables, we will have to do so.
Types of Naive Bayes:
Naive Bayes algorithms can be divided into three types:
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
Pixelette Technologies will solve a classic HR analytics problem through these types of the algorithm. One of the most popular methods of multiclass classification is the Naive Bayes algorithm. In terms of the types of Naive Bayes algorithms, we can choose any of them depending on the data we are trying to analyze. Despite being quite popular, Naive Bayes stands out from the crowd of classification algorithms. Due to its ability to adapt quickly to changing data, it is faster than Random Forest. It is more efficient than logistic regression as well if the assumptions are true for Naive Bayes.
Here are some of the main advantages:
- You can understand it quickly and easily
- There are no overfitting problems with it
- It does not require a lot of training data
Advantages of Naive Bayes:
Drawbacks of Naive Bayes:
- When the number of features is very high, it does not work that well
- There may be some instances where the principle that input features are independent is not valid
- Discretely expressing the continuous variables might lead to information loss.
Even though Naive Bayes is relatively straightforward to understand, its power cannot be underestimated! Text classification tasks can be performed with the software, as it's fast, intuitive, and easy to use. In addition to being able to handle multiclass classification, it is considered a very versatile and flexible classifier. The Naive Bayes classifier is the baseline model in the majority of research papers on text classification.
Applications of Naive Bayes:
Our developed hybrid learning method ensures the precision of classification. It works with precision with any data classification model.
It allows you to find and recover information from complicated data inferences.
We use the best predictive method in a hybrid learning framework for correct classification and regression.
It can fit in a wide range of industries, ranging from supply chain management to robotics.