Machine learning models are the core components that enable machines to learn from data and make predictions or decisions. In this section, we will explore some of the most commonly used machine learning models.
Supervised Learning
Supervised learning models are trained on labeled data, meaning that the input data is paired with the correct output. The goal of supervised learning is to learn a mapping from the input to the output.
Types of Supervised Learning Models
- Linear Regression: A model that predicts a continuous value based on input features.
- Logistic Regression: A model that predicts a binary outcome based on input features.
- Decision Trees: A tree-like model that makes decisions based on features and splits the data into subsets.
- Random Forest: An ensemble method that combines multiple decision trees.
- Support Vector Machines (SVM): A model that finds the best hyperplane to separate data into different classes.
- Neural Networks: A model inspired by the human brain that can learn complex patterns in data.
Unsupervised Learning
Unsupervised learning models are trained on unlabeled data, meaning that the input data does not have corresponding output labels. The goal of unsupervised learning is to find hidden patterns or structures in the data.
Types of Unsupervised Learning Models
- Clustering: A method that groups similar data points together based on their features.
- Association: A method that finds interesting relationships between variables in large databases.
- Dimensionality Reduction: A technique that reduces the number of variables in a dataset while retaining the most important information.
Deep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers. It has become particularly popular in recent years due to its ability to process large amounts of data and make accurate predictions.
Types of Deep Learning Models
- Convolutional Neural Networks (CNN): A model designed to process data with a grid-like topology, such as images.
- Recurrent Neural Networks (RNN): A model that processes sequences of data, such as time series or text.
- Generative Adversarial Networks (GAN): A model that generates new data by competing against a discriminator.
For more information on machine learning models and their applications, you can visit our Machine Learning Documentation.