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Classification and regression are two fundamental types of machine learning problems, each with its own distinct characteristics and objectives: Classification: Classification is a type of supervised learning problem where the goal is to assign a label or category to an input data point based on its features. In other words, the aim is to predict which class or category an instance belongs to. The output of a classification algorithm is a discrete class label. Key features of classification problems include: Output: The output is categorical, representing classes or categories (e.g., "spam" or "not spam"). Objective: The primary goal is to classify new instances into predefined classes based on patterns learned from training data. Evaluation Metrics: Classification algorithms are evaluated using metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Examples: Email spam detection, sentiment analysis, image classification (identifying objects in images), medical diagnosis (disease or not), etc. Algorithms: Common algorithms include decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks. Regression: Regression is another type of supervised learning problem where the goal is to predict a continuous numerical value (output) based on input features. In regression, the algorithm learns the relationship between input variables and a continuous target variable. Key features of regression problems include: Output: The output is a continuous value (e.g., predicting house prices, temperature, sales, etc.). Objective: The primary goal is to make accurate predictions of numerical outcomes based on learned patterns from training data. Evaluation Metrics: Regression algorithms are evaluated using metrics like mean squared error (MSE), root means squared error (RMSE), mean absolute error (MAE), and R-squared. Examples: House price prediction, stock price prediction, temperature forecasting, sales prediction, etc. Algorithms: Linear regression, polynomial regression, support vector regression, decision trees for regression, and various machine learning algorithms can be used for regression tasks. In summary, classification focuses on assigning categorical labels to data points, while regression involves predicting continuous numerical values. Both classification and regression are important components of machine learning and are used in a wide range of applications across various domains. Visit Here: https://nareshit.com/data-science-online-training/ contact us: online@nareshit.com|+91-8179191999