One of the most frequent predictions is the classification problem, in which the data is the dependent variable that is clearly defined and has distinct categories that the model must be able to determine. The majority of algorithms are trained to create an appropriate decision boundary that aids in classifying data by providing the data with distinct labels for each class. 

The settings of the algorithms are frequently updated, and various methods are employed to minimize errors that occur when an error is a misclassification, meaning that it assigns the wrong data label to an individual data point. 

The most widely used algorithms for learning are Logistic Regression, which is a Linear Supervised Learning algorithm. A Linear Classification algorithm is where the dependent variable is defined based on an equation that is linearly combined with the independent variables. The decision boundary, in this case, is a straight line or hyperplane with a straight line within the data space that separates the data points that classify the data. Regularized Logistic Regression is another method employed to address the problem of overfitting. Other advanced Linear classification algorithms include SVM, Naive Bayes, etc. 

There are a variety of non-linear classification techniques, like the instance-based KNN and ANN, that can be modified to function in a Supervised Learning setup by using Backpropagation (Note that ANN technically operates in a Reinforcement Learning environment in which the algorithm can be improved through trials and errors). In these algorithms, the decision boundary is not linear. 

Additionally, diverse ensemble techniques, such as Bagging, Boosting, and Stacking, may be employed to solve classification problems, employing different algorithms and resampling strategies to give more reliable results.