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Classification is a process of finding a function which helps in dividing the dataset into classes based on different parameters. In Classification, a computer program is trained on the training dataset and based on that training; it categorizes the data into different classes. The task of the classification algorithm is to find the mapping function to map the input(x) to the discrete output(y). Example: The best example to understand the Classification problem is Email Spam Detection. The model is trained on the basis of millions of emails on different parameters, and whenever it receives a new email, it identifies whether the email is spam or not. If the email is spam, then it is moved to the Spam folder. Regression is a process of finding the correlations between dependent and independent variables. It helps in predicting the continuous variables such as prediction of Market Trends, prediction of House prices, etc. The task of the Regression algorithm is to find the mapping function to map the input variable(x) to the continuous output variable(y). Example: Suppose we want to do weather forecasting, so for this, we will use the Regression algorithm. In weather prediction, the model is trained on the past data, and once the training is completed, it can easily predict the weather for future days. OR Classification is the process of finding or discovering a model (function) which helps in separating the data into multiple categorical classes. In classification, the group membership of the problem is identified, which means the data is categorized under different labels according to some parameters and then the labels are predicted for the data. Regression is the process of finding a model or function for distinguishing the data into continuous real values instead of using classes. Mathematically, with a regression problem, one is trying to find the function approximation with the minimum error deviation. In regression, the data numeric dependency is predicted to distinguish it. The Regression analysis is the statistical model which is used to predict the numeric data instead of labels. It can also identify the distribution movement depending on the available data or historic data. OR Key Differences between Classification and Regression The Classification process models a function through which the data is predicted in discrete class labels. On the other hand, regression is the process of creating a model which predicts continuous quantity. The classification algorithms involve decision tree, logistic regression, etc. In contrast, regression tree (e.g. Random forest) and linear regression are the examples of regression algorithms. Classification predicts unordered data while regression predicts ordered data. Regression can be evaluated using root mean square error. On the contrary, classification is evaluated by measuring accuracy.

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