Machine Learning Models

A machine learning model is a mathematical representation of a system capable of learning from data and making predictions or decisions. A machine learning model aims to capture patterns and relationships in data in a way that allows it to make accurate predictions on new and unseen data.

Some different types of standard machine learning models include linear and logistic regression, decision trees, random forests, support vector machines, and neural networks. The choice of model depends on the type of problem being solved and the data’s attributes.

A machine learning model consists of a set of parameters learned from training data and an algorithm that uses the parameters to make predictions. Once a model has been trained, it can be used to predict new and actual data. Machine learning models can be used for various applications, including image classification, speech recognition, and natural language processing (NLP). Machine learning models are being adopted as tools for automating decision-making and for discovering patterns and insights in data.