Top 10 Most Commonly used Supervised Learning Algorithms

Introduction

Supervised learning is a type of Machine Learning Algorithm that uses a labeled dataset to learn how to predict the output of new data. The labels are provided by a human and are used to train the model. The model is then tested on new data to see how accurate it is.

There are many different types of supervised learning algorithms, but some of the most commonly used ones are listed below.

1. Linear Regression

This Algorithm is used to find a linear relationship between a dependent variable and one or more independent variables. It is a popular algorithm for solving regression problems.

2. Logistic Regression

This Algorithm is used to predict a binary outcome (yes/no, 1/0) based on one or more independent variables. It is a popular algorithm for solving classification problems.

3. Support Vector Machines

This Algorithm is used to find the best line or hyperplane that separates data points in a dataset. It is a popular algorithm for solving classification and regression problems.

4. Decision Trees

This Algorithm is used to create a model of decisions based on data. It is a popular algorithm for solving classification and regression problems.

5. Naive Bayes

This Algorithm is used to predict the probability of an event based on prior knowledge. It is a popular algorithm for solving classification problems.

6. K-Nearest Neighbors

This Algorithm is used to find the K nearest neighbors of a data point. It is a popular algorithm for solving classification and regression problems.

7. Neural Networks

This Algorithm is used to create a model that can learn and make predictions. It is a popular algorithm for solving classification and regression problems.

8. Random Forests

This Algorithm is used to create a model that can learn and make predictions. It is a popular algorithm for solving classification and regression problems.

9. Gradient Boosting Machines

This Algorithm is used to create a model that can learn and make predictions. It is a popular algorithm for solving classification and regression problems.

10. Principal Component Analysis

This Algorithm is used to find the directions of maximum variance in a dataset. It is a popular algorithm for solving regression problems.

Conclusion

Each of these Algorithms has its own strengths and weaknesses, so it’s important to choose the right one for your task. In general, more complex algorithms (like neural networks) can learn more complex patterns, but they are also more likely to overfit on the training data. Simple algorithms (like linear regression) are less likely to overfit, but they may not be able to learn complex patterns.

The best way to choose an Algorithm is to experiment with a few different ones on your dataset and see which one gives the best results.