**Difference Between Linear Regression and Logistic Regression**: The two main** types of regression** are** linear regression and logistic regression**.** Logistic regression** is only used with binary dependent variables.

The **linear regression** line is used when the conditional variable is continuous and the regression line is linear.

### What is Regression:

The **regression** method is a statistical method that helps us analyze and understand relationships between two variables.

In performing regression analysis, the process assists in understanding which factors are essential, which elements can be ignored, and how they influence each other.

### Linear Regression:

**Linear regression** is the easiest machine learning algorithm to assess and deploy. Because this is a **supervised learning algorithm**, an excellent labeled dataset is necessary to predict continual values.

This machine-learning algorithm is driven by linearity. **Linear regression** draws straight lines using the information fed into the algorithm to predict future discounts.

**y= a0+a1x+ c**

### Types of Linear Regression

- Forward Stepwise Regression
- Stepwise Regression
- Linear Splines
- Simple Linear Regression
- Multiple Linear Regression
- Stepwise Selection
- Backward Stepwise Regression

**Logistic Regression:**

**Logistic Regression, **This technique uses binary dependent variables, true or false, 0 or 1, yes or no, which means that the outcome can only be true or false.

Using it, for example, is useful when we need to calculate the probability of an event succeeding or failing. Here, a sigmoid function is added to the formula, and Y is a number between 0 and 1.

Within the identity, the phrase logistic refers again to the activation procedure employed in this regression.

The activation performs, or the logistic performs, in this case, are nothing but the sigmoid performs. Sigmoid function the logistic regression value is maintained all times between zero and one.

**Types of Logistic Regression**

- Multinomial Logistic Regression
- Binary Logistic Regression
- Ordinal Logistic Regression/Proportional odds model

## Key Difference Between Linear Regression and Logistic Regression

### Linear Regression

- A linear regression algorithm could predict a price in the range of destructive infinity to constructive infinity.
- Linear regression does not require activation.
- Linear regression doesn’t have a threshold worth.

### Logistic Regression:

- It also requires that the information fed into it is adequately labeled. Nonetheless, this algorithm is used to classify as an alternative to regression. It is a supervised classification method.
- Logistic regression predicts that the outcome will be between 0 and 1. With the help of a threshold value, this function allows for a straightforward classification.
- Activation is what we’re looking for here. It’s the sigmoid that performs.
- In logistic regression, the correct course of each event is determined by a threshold value.