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.