16, October,2023

Logistic Regression (classification) :
Logistic Regression is a statistical method used for modeling the probability of a certain class or event existing. It is used when the dependent variable is binary (i.e., it has two possible outcomes).

  • Fundamentals:
    • While linear regression predicts a continuous output, logistic regression predicts the probability of an event occurring.
    • It uses the logistic function (S-shaped curve) to squeeze the output of a linear equation between 0 and 1.
  • Coefficients:
    • Each coefficient represents the change in the log odds of the output for a one-unit change in the predictor.
    • Positive coefficients increase the log odds of the response (and thus increase the probability), and negative coefficients decrease the log odds of the response (decreasing the probability).
    • The interpretation requires an understanding of log odds (logit function).
  • Applications:
    • Credit approval, medical diagnosis, and election prediction are some areas where logistic regression can be applied.

Logistic Regression & Logistic Regression Details Pt1: Coefficients:

The StatQuest videos provide a visual and intuitive understanding of logistic regression.

  • Key Takeaways from the Videos:
    • The logistic function ensures output values are between 0 and 1, making it suitable for probability estimation.
    • The video discusses how to interpret the coefficients in logistic regression, with an emphasis on understanding the odds ratio.
    • It demystifies the math behind logistic regression, making it easier to grasp for those new to the concept.

Leave a Reply

Your email address will not be published. Required fields are marked *