Explain how the following work, and their pros and cons:
classification trees
regression trees
random forests
bagging and boosting
Solution
classificationtrees: which anticipates an objective variabledepending on many input variables. They can manage predictorvariables which are steady.
Pros
.Simple to elucidate and describe.
.Finds the communications between variables.
Regressiontrees: Permits input variables as an amalgam ofnon-stop variables.
Pros
.Capable to manage the data which is absent withoutany troubles.
. A simpler comprehensible manner to pass a style fornon-statistician
cons
.Leans to contain the big variance i.e a little modification indata may generate a various sequence of splits.
.Absence of the inferential process
randomforests An ensemble process for partition,regression, and further activities which