Supervised learning is a type of machine learning where a model is trained on labeled data. It is widely used in various applications, such as image recognition, natural language processing, and predictive analytics. In this article, we will discuss the basics of supervised learning, its types, and some common algorithms.
Types of Supervised Learning
1. Classification
Classification is a task of predicting the class of an observation based on the features provided. It is commonly used for binary classification (two classes) and multi-class classification (more than two classes).
- Binary Classification: The target variable has two classes. For example, spam or not spam, cat or dog.
- Multi-Class Classification: The target variable has more than two classes. For example, species classification, sentiment analysis.
2. Regression
Regression is a task of predicting a continuous value based on the features provided. It is commonly used for predicting numerical values, such as house prices, stock prices, and temperatures.
- Linear Regression: It is the simplest form of regression where the relationship between the input and output variables is linear.
- Polynomial Regression: It uses polynomial functions to model the relationship between the input and output variables.
- Logistic Regression: It is a type of regression that is used for binary classification tasks.
Common Algorithms
1. Linear Regression
Linear regression is a simple and widely used algorithm for regression tasks. It assumes a linear relationship between the input and output variables.
2. Logistic Regression
Logistic regression is used for binary classification tasks. It calculates the probability of an observation belonging to a particular class.
3. Decision Trees
Decision trees are a non-parametric supervised learning algorithm that can be used for both classification and regression tasks. They are based on a tree-like model of decisions and their possible consequences.
4. Support Vector Machines (SVM)
Support Vector Machines is a powerful supervised learning algorithm that is used for both classification and regression tasks. It finds the best hyperplane to separate the data into classes.
5. Neural Networks
Neural networks are a series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
More on Machine Learning
To learn more about machine learning and its applications, check out our Machine Learning section.