Which one is NOT one of the disadvantages of decision tree? Group...

Which one is NOT one of the disadvantages of decision tree?

Group of answer choices

In a complex model, it may result to an overfitting

Small variations in features may affect the trees

It is not useful when we have targets with more than 2 levels

If some of the target variables overcome the others, it may result in biased trees

Which of the following statements is correct?

Group of answer choices

Multivariable and Multivariate regression are the same thing, i.e. each involve multiple features (independent variables) and one response (dependent variable).

If we complete an analysis and find that there is statistically significant evidence of a relationship between a predictor variable and its corresponding response variable we can safely assert that we know the predictor caused the response.

Decision tree induction algorithms use what is called a greedy strategy but then may only find a local minimum.

There is no way to make a 2-way split on continuous data.