This algorithm learns from labelled data. It uses input data and corresponding output values to train the model. After training the model, it can predict output values for new data. Supervised learning can be divided into two major types:
- Regression: In supervised learning, a regression algorithm is used to predict a continuous output variable. It maps input values to a continuous output range. The algorithm learns from a labelled dataset and uses this knowledge to predict the output value for unseen data. Some examples of regression algorithms are linear regression, polynomial regression, and support vector regression.
- Classification: In supervised learning, the classification algorithm is used to predict a categorical output variable. It maps input values to discrete(finite set) output values. The algorithm learns from a labelled dataset and uses this knowledge to predict the class of unseen data. Some examples of classification algorithms are logistic regression, decision trees, and support vector machines.
Some of the techniques and algorithms used in supervised learning include:
- Decision Trees: This is a popular algorithm used in classification tasks. It involves dividing the input space into regions and assigning a class label to each region.
- Support Vector Machines (SVM): This is a powerful algorithm used in both classification and regression tasks. It finds the hyperplane that maximizes the margin between the classes.
- Random Forest: This is an ensemble learning algorithm that combines multiple decision trees to improve the accuracy and robustness of the model.
- Neural Networks: This is a family of algorithms that are used for both classification and regression tasks. They are particularly effective in handling high-dimensional data.
- Naive Bayes: This is a simple yet effective algorithm used in classification tasks. It is based on Bayes' theorem and assumes that the features are independent of each other.
Regression & Classification
Advanced Learning Algorithms & Neural Networks