Python for Machine Learning - The Complete Beginners Course - Logistic Regression Versus Linear Regression

The video tutorial explains the differences between logistic and linear regression. It highlights that logistic regression is used for categorical variable estimation, while linear regression is for continuous variables. Logistic regression uses an S curve for categorization, employing a sigmoid function, whereas linear regression seeks a best-fit line for prediction. The video also discusses the necessity of linear relationships in linear regression and the absence of collinearity in logistic regression. A quiz at the end reinforces that logistic regression is primarily a classification tool.

Which type of regression is used to estimate the outcome of continuous variables?

• Linear Regression

• Logistic Regression

• Polynomial Regression

• Ridge Regression

What function is used in logistic regression to find the S curve?

• Linear Function

• Quadratic Function

• Sigmoid Function

• Exponential Function

In linear regression, what is the relationship between dependent and independent variables?

• Non-linear

• Linear

• Exponential

• Logarithmic

Which regression type requires no collinearity between independent variables?

• Ridge Regression

• Logistic Regression

• Polynomial Regression

• Linear Regression

Is logistic regression mainly used for regression?

• Sometimes

• Depends on the context

• False

• True