What is the difference between classification and regression?

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Classification is used to produce discrete results, classification is used to classify data into some specific categories .for example classifying e-mails into spam and non-spam categories.
Whereas, We use regression analysis when we are dealing with continuous data, for example predicting stock prices at a certain point of time.

Classification and regression are two fundamental types of supervised machine learning tasks, each with distinct objectives and methodologies:

  1. Objective:
    • Classification: In classification, the goal is to predict the categorical class labels of new instances based on past observations. The output is discrete, representing a class or category.
    • Regression: In regression, the goal is to predict a continuous numeric value based on input features. The output is a continuous value, such as a price, temperature, or probability.
  2. Output:
    • Classification: The output is a class label or category. For example, predicting whether an email is spam or not spam, or classifying images into different categories like cats or dogs.
    • Regression: The output is a real-valued number. For example, predicting house prices based on features like size, location, and number of bedrooms.
  3. Evaluation:
    • Classification: Evaluation metrics for classification tasks typically include accuracy, precision, recall, F1-score, and confusion matrix.
    • Regression: Evaluation metrics for regression tasks often include mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and R-squared.
  4. Algorithm Selection:
    • Classification: Algorithms commonly used for classification include logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and neural networks.
    • Regression: Algorithms commonly used for regression include linear regression, decision trees, random forests, support vector regression (SVR), k-nearest neighbors (KNN), and neural networks.
  5. Output Interpretation:
    • Classification: Output is interpreted as the probability or confidence of belonging to each class. The class with the highest probability is often chosen as the predicted class label.
    • Regression: Output is interpreted as the predicted value itself.

In summary, while both classification and regression are supervised learning techniques used to make predictions, they differ in terms of the nature of the output (discrete vs. continuous), evaluation metrics, algorithm selection, and output interpretation.