Regression Algorithms

There are many different types of regression algorithms, but some of the most common include:

• Linear Regression
  • Linear regression is one of the simplest and most widely used statistical models. This assumes that there is a linear relationship between the independent and dependent variables. This means that the change in the dependent variable is proportional to the change in the independent variables.
• Polynomial Regression
  • Polynomial regression is used to model nonlinear relationships between the dependent variable and the independent variables. It adds polynomial terms to the linear regression model to capture more complex relationships.
• Support Vector Regression (SVR)
  • Support vector regression (SVR) is a type of regression algorithm that is based on the support vector machine (SVM) algorithm. SVM is a type of algorithm that is used for classification tasks, but it can also be used for regression tasks. SVR works by finding a hyperplane that minimizes the sum of the squared residuals between the predicted and actual values.
• Decision Tree Regression
  • Decision tree regression is a type of regression algorithm that builds a decision tree to predict the target value. A decision tree is a tree-like structure that consists of nodes and branches. Each node represents a decision, and each branch represents the outcome of that decision. The goal of decision tree regression is to build a tree that can accurately predict the target value for new data points.
• Random Forest Regression
  • Random forest regression is an ensemble method that combines multiple decision trees to predict the target value. Ensemble methods are a type of machine learning algorithm that combines multiple models to improve the performance of the overall model. Random forest regression works by building a large number of decision trees, each of which is trained on a different subset of the training data. The final prediction is made by averaging the predictions of all of the trees.

Regularized Linear Regression Techniques

• Ridge Regression
  • Ridge regression is a type of linear regression that is used to prevent overfitting. Overfitting occurs when the model learns the training data too well and is unable to generalize to new data.
• Lasso regression
  • Lasso regression is another type of linear regression that is used to prevent overfitting. It does this by adding a penalty term to the loss function that forces the model to use some weights and to set others to zero.

Characteristics of Regression

Here are the characteristics of the regression:

• Continuous Target Variable: Regression deals with predicting continuous target variables that represent numerical values. Examples include predicting house prices, forecasting sales figures, or estimating patient recovery times.
• Error Measurement: Regression models are evaluated based on their ability to minimize the error between the predicted and actual values of the target variable. Common error metrics include mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
• Model Complexity: Regression models range from simple linear models to more complex nonlinear models. The choice of model complexity depends on the complexity of the relationship between the input features and the target variable.
• Overfitting and Underfitting: Regression models are susceptible to overfitting and underfitting.
• Interpretability: The interpretability of regression models varies depending on the algorithm used. Simple linear models are highly interpretable, while more complex models may be more difficult to interpret.

Examples

Which of the following is a regression task? 

• Predicting age of a person
• Predicting nationality of a person
• Predicting whether stock price of a company will increase tomorrow
• Predicting whether a document is related to sighting of UFOs?

Solution : Predicting age of a person (because it is a real value, predicting nationality is categorical, whether stock price will increase is discrete-yes/no answer, predicting whether a document is related to UFO is again discrete- a yes/no answer).

Regression, a statistical approach, dissects the relationship between dependent and independent variables, enabling predictions through various regression models.

The article delves into regression in machine learning, elucidating models, terminologies, types, and practical applications.