machine learning

In machine learning, regression is a type of supervised learning where the goal is to predict a continuous output variable based on one or more input features. Here are some common types of regression and simple explanations with examples:

  1. Linear Regression:
    • Explanation: It assumes a linear relationship between the input features and the output variable.
    • Example: Predicting house prices based on features like square footage, number of bedrooms, and location.
  2. Polynomial Regression:
    • Explanation: It extends linear regression by considering polynomial relationships between the input features and the output.
    • Example: Predicting the trajectory of a thrown object based on time, considering both linear and quadratic terms.
  3. Ridge Regression (L2 Regularization):
    • Explanation: It adds a penalty term to the linear regression to prevent overfitting by penalizing large coefficients.
    • Example: Predicting stock prices using multiple financial indicators, with regularization to handle multicollinearity.
  4. Lasso Regression (L1 Regularization):
    • Explanation: Similar to Ridge, it adds a penalty term, but Lasso tends to produce sparse models by setting some coefficients to zero.
    • Example: Identifying significant features in a dataset when predicting customer churn.
  5. Logistic Regression:
    • Explanation: Despite its name, logistic regression is used for binary classification problems, predicting the probability of an instance belonging to a particular class.
    • Example: Predicting whether an email is spam or not based on various features.
  6. Support Vector Regression (SVR):
    • Explanation: A regression technique that uses support vector machines to find a hyperplane that best fits the data while minimizing errors.
    • Example: Predicting the price of a commodity based on various economic factors.

These regression techniques cater to different scenarios, and the choice depends on the nature of your data and the problem you’re trying to solve.

Discover more from Susiloharjo

Subscribe now to keep reading and get access to the full archive.

Continue reading