R-Programming: Logistic and Poisson regression
In the last part of our R-programming series, we covered ANOVA, now it’s time to shift our focus to logistic and Poisson regression. Logistic regression is among one of the most famous algorithms in the entire classical machine learning. Logistic regression is still in use by companies like Google due to its fast prediction time. This is the last blog on the R-programming series and this will cover the following topics:
- Multiple logistic regression
- Interpreting logistic regression
- Goodness of fit
- ROC curve
- Poisson regression model
Multiple logistic regression
Logistic regression is a process of modeling the probability of a discrete outcome given an input variable. The most common logistic regression models a binary outcome; something that can take two values such as true/false, yes/no, and so on.
To know the maths behind logistic regression:
Let’s directly jump into R studio and run our LR model.
data("Titanic")
titanic <- data.frame(Titanic)H0: Sex has an effect on survival of Titanic passenger…
