Many machine learning applications have to create precise categories. Knowing the categories information is in is critical for suitable prediction and sorting. They can turn a jumbled set of numbers into a coherent group as well as a linear regression line or a group of means. The naive Bayes classifier algorithm is an example of a categorization algorithm used frequently in data mining. This algorithm can be used for a multitude of different purposes that all tie back to the use of categories and relationships within vast datasets.
Naive Bayes Classifier Defined
The Naive Bayers classifier is a machine learning algorithm that is designed to classify and sort large amounts of data. It is fine-tuned for big data sets that include thousands or millions of data points and cannot easily be processed by human beings. This algorithm works by analyzing a point in a dataset with a number of different criteria. The criteria involved include attributes and thresholds that are associated with a category.
Naive Bayes is specifically “naive” because it is not weighing these criteria independently. There is no analysis of the nature of these categories or the meaning of a data point following some criteria and not others. The algorithm is simply weighing them together in order to place a data point in one category or another. Output for a naive Bayes classifier algorithm consists of a data set placed into a number of different categories. Category composition and the speed of the function can also be determined. There is no further quantitative or qualitative analysis performed. That function can then be repeated again and again in order for machine learning to occur.
Naive Bayes Classifier Types
The Naive Bayes Classifier algorithm, like other machine learning algorithms, requires an artificial intelligence framework in order to succeed. This framework must be flexible and able to learn and improve relatively quickly. It must also have demonstrable attributes that make machine learning and tweaking the system relatively easy.
The most widely used artificial intelligence architecture is the artificial neural network. Artificial neural networks are modeled off of the human brain and consist of a series of connected nodes. With the naive Bayes classifier algorithm, the nodes may be individual categories or steps in the function. The artificial neural network works by entering inputs and then examining the outputs created by the structure. This machine learning algorithm then changes the weights of the nodes depending on how close the output gets to the goals of the algorithm.
A machine learning algorithm such as the naive Bayes classifier can learn in many different ways. One is through supervised learning. In the process of supervised learning, an artificial intelligence system attempts to replicate the information of an example set. A naive Bayes classifier system would have a data set in a group of categories as its output. The artificial intelligence system would then run the algorithm again and again in an attempt to produce an output that was within a margin of error from the example set. Then, the system could be used to predict and analyze future sets of data.
Unsupervised learning happens in a somewhat different way. With this approach to machine learning, there is no example set that the algorithm is attempting to replicate. Instead, there is only a series of guidelines that helps to structure the algorithm’s work. In the case of the naive Bayes classifier, the guidelines may be a broad number of categories or maximum/minimum amounts for each category.
The algorithm then works over a period of time and fills categories based on guidelines. Operator input is required for significant changes to the output of the algorithm. Unsupervised learning in this matter is meant to explore different avenues and relationships inside of the data that machine learning operators had not otherwise detected.
Naive Bayes Classifier Uses
This classifier is a tool for data organization and analysis. Not surprisingly, it has a number of different uses throughout the field of data mining. The main uses are to categorize and predict. Categorization is the main point of any classifier. It can be used to make sense of massive reams of data that otherwise seem disparate. Categories help point to similarities and differences between different data points in a set.
They allow the data mining analyst to make a number of assumptions about particular data points and the data set as a whole. Once the data is in categories, it can more easily be visualized and described. Categories themselves can be compared, prioritized, or discarded depending on the situation involved.
Another primary use of this algorithm is prediction. The algorithm can be used to place more and more data sets into particular categories. That process can then be tweaked through the artificial intelligence framework. A successful naive Bayes classifier can sort thousands of data points into hundreds of categories with a low error rate in an incredibly short period of time. This information can be used to predict how future datasets will respond and what attributes and criteria they may have.
Thoughts on Naive Bayes Classifier Algorithm
The naive Bayes classifier may not be the most well-known and visible of artificial intelligence algorithms. However, its ability to quickly process vast quantities of information is nearly unmatched. This classifier can be critical for individuals looking to study data with the processing power of large computers, and the learning potential of artificial intelligence, behind them.