Regression Basics in Machine Learning 101

Regression analysis is a statistical process for estimating the relationships between a dependent variable and one or more independent variables. It is used to understand how the dependent variable is affected by the independent variables. Regression analysis is a powerful tool that can be used to make predictions, understand relationships, and make decisions.

In this blog post, we will provide a basic overview of regression analysis. We will discuss the different types of regression models, the assumptions of regression analysis, and how to interpret regression results.

Types of Regression Models

There are many different types of regression models, each with its own strengths and weaknesses. The most common types of regression models include:

• Simple linear regression: This is the simplest type of regression model, and it assumes that the relationship between the dependent variable and the independent variable is linear.
• Multiple linear regression: This type of regression model assumes that the relationship between the dependent variable and the independent variables is linear, but it allows for more than one independent variable.
• Logistic regression: This type of regression model is used to predict the probability of an event occurring.
• Poisson regression: This type of regression model is used to predict the number of events that will occur in a given period of time.

Assumptions of Regression Analysis

Regression analysis is based on a number of assumptions. These assumptions include:

• Linearity: The relationship between the dependent variable and the independent variables is linear.
• Homoscedasticity: The variance of the errors is constant.
• Independence: The errors are independent of each other.
• Normality: The errors are normally distributed.

Interpreting Regression Results

The results of a regression analysis can be used to make predictions, understand relationships, and make decisions. The following are some of the most important things to consider when interpreting regression results:

• The coefficient of determination (R2): This statistic measures the proportion of variance in the dependent variable that is explained by the independent variables.
• The p-value: This statistic tests the statistical significance of the relationship between the dependent variable and the independent variables.
• The confidence intervals: These intervals provide an estimate of the range of values within which the true value of the coefficient is likely to fall.

Conclusion

Regression analysis is a powerful tool that can be used to make predictions, understand relationships, and make decisions. By understanding the different types of regression models, the assumptions of regression analysis, and how to interpret regression results, you can use this powerful tool to gain insights into your data.