- Decision Trees
- Neural Networks (back propagation)
- Probabilistic networks
- Nearest Neighbor
- Support vector machines
The five popular algorithms in machine learning are:
- Linear Regression: A basic and widely used algorithm for supervised learning, particularly for regression tasks, where the goal is to predict a continuous outcome based on one or more input features.
- Logistic Regression: Another fundamental algorithm used for binary classification tasks, where the output is a binary outcome (e.g., true/false, yes/no, etc.), typically represented as probabilities.
- Decision Trees: Versatile algorithms that can perform both classification and regression tasks. They partition the feature space into a set of simple decision rules inferred from the data.
- Support Vector Machines (SVM): Effective for both classification and regression tasks. SVM aims to find the hyperplane that best separates the classes in the feature space, maximizing the margin between classes.
- K-Nearest Neighbors (KNN): A simple yet powerful algorithm used for both classification and regression tasks. KNN predicts the class or value of a new data point by averaging the values of its k nearest neighbors in the feature space.
These algorithms represent a foundational set in the machine learning toolbox and are commonly taught and employed across various domains and applications.