Logistic Regression in Machine Learning
Logistic regression is is a powerful tool for decision-making. It is a supervised machine learning algorithm mainly used for classification tasks where the goal is to predict the probability that an instance of belonging to a given class or not. It is referred to as regression because it takes the output of the linear regression function as input and uses a sigmoid function to estimate the probability for the given class. It is a kind of statistical algorithm that analyzes the relationship between a set of independent variables and the dependent binary variables.
The difference between linear regression and logistic regression is that linear regression output is the continuous value that can be anything while logistic regression predicts the probability that an instance belongs to a given class or not.
Logistic Regression can be classified into three types:
- Binomial: In binomial Logistic regression, there can be only two possible types of the dependent variables, such as 0 or 1, Pass or Fail, etc.
- Multinomial: In multinomial Logistic regression, there can be 3 or more possible unordered types of the dependent variable, such as “cat”, “dogs”, or “sheep”
- Ordinal: In ordinal Logistic regression, there can be 3 or more possible ordered types of dependent variables, such as “low”, “Medium”, or “High”.
