The goal of this post is to present the most popular supervised learning algorithms.
There are two types supervised learning algorithms: regression and classification. Both of them have the goal to make a prediction based on input data provided during the training. In that input data we have information about the independent variables and dependent variables. The dependent variable is what the algorithm will predict later using new data.
Regression: The dependent variable is a number.
- Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- K-nearest neighbors (KNN)
- Decision Trees & Random Forest
Classification: The dependent variable is a category.
- Logistic Regression (Binary classification)
- Naive Bayes
- Support Vector Machine (SVM)
- K-nearest neighbors (KNN)
- Decision Trees & Random Forest
Juan Carlos Sánchez's Blog 