A classification problem typically involves the classification of an outcome into predefined categories. The outcome is called a dependent variable(s). The outcome is dependent on a set of input variables or features. The input variables or features are called independent variables. Classification problems train the model to mathematically map the combination of independent variables to the dependent variable(s). The output is one of the values within the set. For example, a fruit image, when passed through the classification algorithm, is classified as an apple or an orange. Typically, the algorithms prescribe the probability of the image belonging to a particular class. The class with the maximum probability is the classification based on the training data.