Decision tree
Classification trees are used to separate the data into classes belonging to the response variable. The response variable usually has two classes: Yes or No (1 or 0) and sunny or rain. If the target variable has more than two categories, then C4.5 can be applicable. C4.5 improves the ID3 algorithm for the continuous attributes, the discrete attributes, and the post construction process.
Similar to most learning algorithms, the classification tree algorithm analyzes a training set and then builds a classifier based on that training so that with new data in the future, it can classify the training as well as the new data correctly. A test example is an input object, and the algorithm must predict an output value. Classification trees are used when the response or target variable is categorical in nature.
On the contrary, regression trees are needed when the response variable is continuous and not discrete. For example, the predicted price of a product. A regression tree is built...
