Linear Regression
Linear regression is a fundamental supervised learning algorithm used for predicting continuous numerical values. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data points. The algorithm estimates the coefficients of the equation to make predictions on new data.
Logistic Regression
Logistic regression is a widely used algorithm for binary classification problems. It estimates the probability of an event occurring by fitting a logistic function to the input variables. It is particularly useful when the dependent variable is categorical and has two possible outcomes. Logistic regression can also be extended to handle multiclass classification problems.
Decision Trees and Random Forests
Decision trees are versatile supervised learning algorithms that can be used for both classification and regression tasks. They partition the feature space into regions based on the values of input features, and each region represents a decision or prediction. Random forests are an ensemble method that combines multiple decision trees to improve accuracy and reduce overfitting.
K-nearest Neighbors (KNN)
K-nearest neighbors is a non-parametric algorithm used for classification and regression tasks. It makes predictions based on the k nearest data points in the training set. The algorithm assigns a class or predicts a value based on the majority vote or average of the k nearest neighbors. KNN is a simple yet powerful algorithm that can handle both numerical and categorical data.
Support Vector Machines (SVM)
Support Vector Machines are supervised learning models used for classification and regression analysis. SVMs construct hyperplanes or sets of hyperplanes in a high-dimensional feature space that can be used for decision-making. The goal of SVM is to find the best possible separation between different classes or the best possible fitting line for regression problems.
