Types of Supervised Learning

Supervised Learning has been broadly classified into two types:

1. Regression

In this method, single-output values are produced using training data. It can predict the value for the new data given to the algorithm. It is used whenever the output required is a number such as money or height etc. 

2. Classification

It has done grouping of the data into classes. While the supervised learning algorithms label input data into two distinct classes is known as classification. Multiple classifications are used while categorizing data into more than two classes.

These two are further divided into various algorithms.

Types of Regression

  1. Linear Regression: This algorithm assumes that there is a linear relationship between two variables i.e., input and output variables of the data. 
  2. Logistic Regression: This algorithm predicts the value for the set of given independent variables. It performs the prediction by mapping the unseen data to the logit function that has been programmed into it.

Types of Classification

  1. Naive Bayes Algorithms:This algorithm assumes that the features of the dataset are all independent of each other. It works great on larger datasets.
  2. Decision Trees: This algorithm classifies based on the value of the features.
  3. Random Forest: It is an ensemble method. This algorithm is operated by constructing a multitude of decision trees and outputs a classification of the individual trees. 
  4. SVM: It stands for support vector machines whose algorithm is based on the statistical learning theory of Vap Nik.