Supervised Learning Algorithms List

A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used later for mapping new examples. The most popular supervised learning tasks are Regression and Classification. The result of solving the regression task is a model that can make numerical predictions. For example Real estate value

An ensemble learning method where weak predictive learners are combined to improve accuracy. Popular techniques include XGBoost, LightGBM and more. Handling of multicollinearity. Handling of non-linear relationships. Effective learning and strong generalization performance. XGBoost is fast and is often used as a benchmark algorithm.

Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle Regression, LARS Lasso, Orthogonal Matching Pur

Before diving into algorithm types, let's briefly review what supervised learning means. In supervised learning, a machine learning model learns to map inputs to outputs based on labeled training data. This means each input in the training set has a corresponding known output also called a label or target. The model uses this data to learn

Supervised learning algorithms can be further divided into two categories depending on the type of output they produce. Regression Algorithms Classification Algorithms Regression Algorithms. Regression algorithms are used to predict a continuous numerical value, such as a house's price or a day's temperature. Different types of regression

1. Supervised Learning. Supervised learning algorithms are trained using labeled data, which means the input data is tagged with the correct output. The goal of these algorithms is to learn a mapping from inputs to outputs, making it possible to predict the output for new data. Common supervised learning algorithms include

Hopefully this list can help others as well. The algorithms are grouped by category. 1. Supervised Learning. Supervised learning algorithms learn from labeled data, where the input-output pairs

Supervised learning algorithms-5 Support vector machine. The Support Vector Machine, or SVM, is a popular Supervised Learning technique that may be used to solve both classification and regression issues. However, it is mostly utilized in Machine Learning for classification problems. The SVM algorithm's purpose is to find the optimum line or

It is one of the simplest and most widely used algorithms in supervised learning. Logistic Regression Logistic regression is a type of supervised learning classification algorithm that is used to predict a binary output variable. Decision Trees Decision tree is a tree-like structure that is used to model decisions and their possible

Supervised Learning Algorithms. Supervised machine learning encompasses various algorithms, each suited for different types of problems. Let's explore some of the commonly used algorithms Linear Regression. Linear regression is a popular algorithm used for predicting continuous output values. It establishes a linear relationship between the