Naive Bayes is a family of simple classification methods based on Bayes’ theorem. They are called “naive” because they assume all features are independent of one another, which is rarely true in practice but often still produces good results. The methods are fast, need little training data and are particularly well suited to text. A classic example is the spam filter that sorts emails based on typical words.
A classifier is a model or method that assigns inputs to one of several predefined categories. Classic variants such as decision trees or naive Bayes are trained on labelled examples; language models can also sort texts based on a description of the categories. Typical uses include spam detection, sorting customer enquiries by topic and categorising receipts. Quality is measured with metrics such as accuracy, precision and recall.
Text classification automatically assigns texts to predefined categories, for example emails by request type, reviews by sentiment or documents by topic. Previously this required training dedicated models on labelled examples; today large language models can often assign categories from a description alone. This makes it possible to pre-sort enquiries and route them to the right team faster.
Logistic regression is a statistical method that predicts the probability of one of two outcomes, such as “buys” or “does not buy”. It weights the input features and converts their sum into a value between 0 and 1 using an S-shaped function. Because the weights are easy to interpret, it is widely used in credit scoring, medicine and marketing, often as a solid baseline for more complex models.
Bayesian networks are graphical models that represent variables as nodes and their probabilistic dependencies as directed arrows. Using Bayes' theorem, they show how new observations change the probability of other events. One example is diagnostic support: estimating the most likely cause from observed symptoms. Their strengths are transparency and the ability to handle uncertain or incomplete data.
