Linear regression vs logistic regression: the difference

The two well-known machine learning algorithms that fall under the category of "supervised learning" are linear regression and logistic regression. Both algorithms use labeled datasets to generate predictions because they are supervised in nature. But their primary distinction is in how they are employed. While logistic regression is used to solve classification problems, linear regression is used to solve regression problems. Below is a table outlining the differences between the two algorithms and their descriptions.

Table of Content
Linear Regression
Logistic Regression 
Classification
Key Distinctions Between Logistic and Linear Regression

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Linear Regression

One of the simplest machine learning algorithms, linear regression is used to solve regression problems and falls under the category of supervised learning.
With the aid of independent variables, it is used to predict the continuous dependent variable.

• Finding the best-fit line that can correctly forecast the output for the continuous dependent variable is the aim of linear regression.
• Simple linear regression is used when only one independent variable is used to make a prediction, and multiple linear regression is used when there are more than two independent variables.
• The algorithm establishes the relationship between the dependent variable and the independent variable by locating the best fit line. Additionally, the relationship must be linear.
• Only continuous values, such as price, age, salary, etc., should be the output of linear regression.

 

Logistic Regression

Under the category of supervised learning techniques, logistic regression is one of the most widely used machine learning algorithms.

• It can be applied to classification and regression issues but is primarily used for classification issues.
• With the aid of independent variables, categorical dependent variables are predicted using logistic regression.
• A Logistic Regression problem's output can only fall between 0 and 1. When determining the probabilities between two classes is necessary, logistic regression can be used. such as true or false, 0 or 1, whether it will rain today, etc.
• Maximum Likelihood estimation serves as the foundation for logistic regression. This estimation suggests that the observed data should be the most likely.
• In logistic regression, the weighted sum of the inputs is passed through an activation function that can map values between 0 and 1. Such an activation function is referred to as a sigmoid function, and the resulting curve is known as an S-curve or sigmoid curve.

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Classification

You can use classification to separate a given input into several pre-established categories. The output is a discrete value, or distinct value, such as 0/1, True/False, or an output label class that has been predefined. Segregating or classifying objects is the process of classification, to put it simply. It is a type of supervised learning technique in which output classes are typically created from input data. It offers a mapping function that transforms input values into discrete output classes that are known. It can produce a variety of outputs and accept a variety of inputs.
 

Key Distinctions Between Logistic and Linear Regression

• Data are modeled using continuous numeric values in linear regression. Logistic regression, in contrast, models the data as binary values.

• For logistic regression, a linear relationship between the dependent and independent variables does not need to be established.

• The independent variables in linear regression can be correlated with one another. On the other hand, the variables in logistic regression cannot be correlated with one another.

A random variable, Y (the response variable), is modeled as a linear function of another random variable, X, in linear regression, which uses a straight line to represent data (predictor variable). Logistic regression, on the other hand, models the probability of the events in bivariate that are essentially occurring as a linear function.

 

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