elect what you think is correct (multiple options are possible)?   A) Logistic regression is a parametric classification algorithm and decision tree   B) Logistic regression is based on a linear combination of parameters as is decision tree   C) Logistic regression is based on a linear combination of parameters and a link function called sigmoid   D) Decision trees tend to have high bias and low variance that random forests fix   E) Decision trees use unsupervised learning

Incorrect Question 11 0 / 6 pts Which of the following supervised learning methods CANNOT use categorical input variables (i.e. categorical features, predictors)? O multinomial logistic regression naive Bayes classifier neural net random forest all the listed methods can use categorical features Olinear regression

Explain Decision Tree Algorithm in Machine learning using an example. Mention thedataset link and python programming details in your answer sheet.

Can random forests be used to fix decision trees that tend to have high bias and low variance?