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Regression Models

In this Machine Learning tutorial, we are going to get the basic understanding of what exactly the Regression Models in Machine Learning.

Regression Models are the most popular among all statistical models which are generally used to estimate the relationship between variables.

There are different types of Regression Models. Out of which below are the most commonly used models for Regression Analysis.

1. Simple Linear Regression

2. Multiple Linear Regression

3. Polynomial Regression

4. Logistic Regression

5. Ridge Regression

6. Lasso Regression

7. ElasticNet Regression

8. Support Vector Regression

These above models will be discussed elaborately in the next upcoming topics in this Machine learning tutorial.

Understanding Regression Analysis

Regression Analysis involves in creating the machine learning models which predict a numeric value. Prediction always happens with a solid machine learning model which estimates the relationship between a dependent variable and Independent variable.

Regression Models (i2tutorials)

From the above graph, it can be understood the Independent or Predictor variable is on the X-axis, Whereas the dependent or response variable is on the Y-axis. Coming to the inclined line is none. other than the Regression Line. And the Plotted data points can be seen as blue colored dots.

 

What are Dependent and Independent Variables?

Dependent Variable: By the name itself we can clearly understand that this variable will vary depending on other variables or other factors.

Dependent variable is also called as Response variable (Outcome).

Example:

Consider the students score in the examination which could vary based on several factors.

 

Independent Variable: This is the variable which is not influenced by other variable rather we can say this variable standalone which can have a quality of influencing others.

Independent variable is also called as Predictor variable (Input).

Example:

 Consider the same example as student score in the examination. Generally, student score will depend on various factors like hours of study, attendance etc. So, the time spent by the student to prepare for examination can be considered as independent variable.

Let us continue to learn the Simple Linear Regression….